Enhancing Safety Studies With Vehicle Insight – Webinar Recap

Introduction:

On Tuesday, October 3, 2023, AirSage hosted a webinar highlighting the benefits and applications of connected vehicle (CV) events data called Enhancing Safety Studies With Vehicle Insight. Speakers included Chris Wichman, Transportation Solutions Advisor at AirSage, and Soroush Salek, Director of Traffic Engineering at CIMA+. Chris spoke about the nuances of CV data and how it’s procured, processed, and cleansed by AirSage, while Soroush explained how he utilized AirSage CV Events data to improve York’s (Ontario, Canada) Regional Safety Plan.

AirSage – A Pioneer in Location Intelligence

To begin, Chris provided some background on AirSage and its more than 20 year history in procuring, processing, and cleansing location data. He explained that AirSage began working with connected vehicle data over a decade ago. Throughout this time, AirSage has focused heavily on privacy and security and remains fully compliant with all relevant regulations (including CPRA and GDPR).

What Is CV Data and Where Does It Come From?

Next, Chris ensured the audience understood the term CV and where it comes from. A CV, or connected vehicle, refers to any vehicle that's equipped with a communication system that allows it to interact either with other vehicles, infrastructure, or an external network.

Chris then described the sources of CV data and shared a chart depicting the attributes of each. He noted that, “irrespective of the source, the core attributes of connected vehicle data (timestamp, coordinates, speed and heading) are really the key to leveraging this data for transportation, engineering, and planning.”

AirSage’s Role

CV data is very big data. Chris explained, “The sheer volume of this data is really the biggest challenge in working with it, particularly for consultants or public agencies.” It is difficult to parse through especially when consultants and agencies have many other competing priorities. That’s where AirSage steps in. AirSage unlocks the value of connected vehicle data and helps clients avoid the headaches of working with it.

AirSage’s Safety Products

Chris listed the two AirSage data products used for transportation safety: vehicle speeds and vehicle events. While AirSage does support link level vehicle data, it also offers trajectory data, which has a better ability to answer very specific and unique questions. Chris shared the map below as an example, which shows how isolating a single vehicle’s trip and string of speed readings can help clients understand how an individual vehicle is a proxy for a driver interacting with the roadway network.

With this disaggregate data, clients can answer questions like: ‘how does the speed of this vehicle change in proximity to an external roadway characteristic, like a curve or a video enforcement camera?’. Chris concluded: “Aggregated statistics can be interesting and meaningful, but disaggregate speeds are powerful in the ability to assess behaviors and risk, say pre- and post-implementation of some safety countermeasure.”

Vehicle events are reported in a separate dataset from the vehicle speeds dataset. They represent movements exceeding a preset G-force threshold above defined time parameters, which triggers the reporting of an event. These include harsh braking, harsh acceleration and harsh cornering events. Chris noted, “There's no standardized definition of what the trigger is of an event across OEMs and TSPs. So, it's important to understand what the thresholds are and the relative sensitivity when working with this type of data.”

 

 

Chris shared an example of a few days of events in Atlanta (image 1) and then zoomed in on one particular interchange (image 2).

   

He explained, “While the frequency zoomed out at a regional scale can help understand the magnitude of risk and help clients do network level screening, the events data also carries attributes for the G force and speed at the time of the event, which can allow for some calculations of a severity metric as well.” The data doesn’t only show where an event has occurred, but it also helps characterize details of the specific event. 

 

Benefits of AirSage to CIMA+

Chris listed three ways AirSage provides benefits to clients like CIMA+: (1) access to quality data, (2) expertise in curating the data down to the geographic area or even corridors of interest for a particular project, and (3) ability to conflate - or align the GPS coordinates - to the client’s preferred roadway network. Chris explained, “For the data output for CIMA+, we included Soroush’s client’s unique segment IDs and intersection IDs, which provided a turnkey ready data set to leverage in his analysis.”

CIMA+ Introduction to York Regional Safety Plan

Soroush began his presentation by sharing some vehicle collision statistics about the York region, including: “In 2018, 2,431 people unfortunately lost their lives on Canadian roads, and 602 of them were in Ontario. 35 of those fatalities occurred in the York region.” When York looked at the past five years of collision data, they saw a general decreasing trend; however, they felt that the number of injury collisions and fatal collisions in York region was still too high, so they contracted with CIMA+ to develop a five-year road safety strategic plan. 

Data Analysis Overview

Soroush explained that following the principles of a safe system approach and the vision zero philosophy, his team looked into six different phases: (1) review of existing safety programs, (2) public and stakeholder consultation, (3) data analysis, (4) incorporating safety in design and planning and developing (5) an action plan and (6) financial plan for the strategy. 

For safety data analysis, the CIMA+ team used a number of data sources and advanced analysis techniques to answer questions such as ‘where are collisions happening, who is involved in collisions and why are they happening?’. Soroush confirmed, “We conducted a variety of analyses such as collision analysis, human factors and analysis, network screening, systemic safety and analysis, safety culture and analysis, and a connected vehicle data analysis.”

CV Data Applications in Safety Planning

Soroush listed various applications of CV data in transportation planning and engineering. He cited traffic operations studies such as travel times studies, traffic diversion and detour planning studies, and transportation planning projects such as regional destination studies. He described, “In recent years, application of connected vehicle data in traveler safety or road safety projects have gained more traction.”

CIMA+ was aware of connected vehicle data applications in network-wide safety analysis, “before-and-after” evaluations of the countermeasures and network wide speed compliance studies, so they decided to use connected vehicle data to support their work on development of the York Traveler Safety Plan.

CIMA+ Obtains AirSage CV Events Data

Soroush’s team obtained CV events data - for May 7 to June 9, 2023 - from AirSage early in the project. The threshold that was used to generate a harsh braking event was 2.5 m/s^2 (~ 0.25G) for a minimum duration of 2 seconds and a maximum duration of 4 seconds. Harsh acceleration had the same threshold but with a minimum duration of 1 second and maximum duration of 5 seconds. 

AirSage provided CIMA+ with  data that was mapped to and aligned with regional facilities, meaning that events were assigned to unique IDs at intersections and roadway segments along the regional transportation network. CIMA+ combined AirSage’s data with the region's road safety database, which already included information that was being used for basic network screening. CIMA+ augmented that data with more facility characteristics that are useful to conduct systemic safety analysis. The team also added collision data, traffic volume data, and the network screening results to the database. Soroush noted, “Just looking at the overall (CV events) data that we received, we noticed that a majority of the data was harsh braking events – 75% - and about a fourth was harsh acceleration.”

CV Events Data Analysis

Distribution of Events

Soroush shared the graph below of the daily distribution of CV Events in the study area. He explained, “As expected on Saturdays and Sundays few events are happening, but there is a peak on Tuesday.”

Another graph prepared by CIMA+ showed the hourly distribution of harsh braking at intersections in blue bars and harsh braking mid block in green bars. What Soroush found interesting was that the peak frequency for harsh braking at intersections generally did not coincide with the AM and PM peak periods; however, harsh braking at mid block locations more closely aligned with the AM and PM peak periods.

Soroush explained that when his team looked at the hourly distribution for harsh acceleration, they found more harsh acceleration events mid block, but again, it did not follow the AM and PM peak trends they were expecting.

CIMA+ mapped AirSage’s CV events data to the regional transportation network. As expected, municipalities with higher populations and traffic volumes had more events in the dataset.

CV Events and Network Screening Priorities

Soroush next shared a map that compared the frequency of harsh braking and harsh acceleration events with the findings of empirical based network screening ranking.

Soroush’s team found that the majority of high priority locations revealed from network screening had a high frequency of both harsh braking and harsh acceleration events. However, some locations not identified by network screening still had a high frequency of events. Soroush confirmed, “These are the locations that can be identified through other ranking systems - one of them being connected vehicle rank.” 

CV Data Stability

CIMA+ tested the AirSage data to see how many days of events data it would take to obtain stability in the frequency of the harsh braking and harsh acceleration events across the network. They generated a video that highlighted the average daily frequency of events across the entire network.

 

The first day of data  showed the number of events made on May 7th. The second day (May 8th) showed that  the number of events on that day plus May 7th divided by 2  and so on. After seven days of data, the distribution of the events on average was not changing, so they had achieved stability. Soroush stated, “This is good news. It tells us that if we have at least 7 consecutive days of data, we can draw some reliable conclusions in support of network wide safety reviews.”

CV Events Modelling

CIMA+ wanted to incorporate CV events in some of their collision prediction models. They wanted to develop separate models where the connected vehicle events were a dependent variable and predict those events using road and traffic operation characteristics. 

They first looked into some correlation indices between connected vehicle data and other variables like collisions, traffic volume, and the facility characteristics. They found that harsh acceleration had a high correlation with the average number of non-fatal collisions, but harsh braking surprisingly did not have a strong correlation.

Then, they looked into the correlation between the events and different impact types. They observed again that harsh acceleration had a significant correlation with variant collision, side swipe collision and turning movement collisions. 

Soroush’s team also explored the correlation between CV events with signalized intersections that have minor AADT, major AADT, total AADT, presence of left turn lane, presence of right turn lane, divided roadway and intersection, the number of lanes on major road, and the potential for safety improvement index from network screening. All of them showed significantly high correlation.

Collision Prediction Models

CIMA+ began developing collision prediction models, using the existing safety performance functions that the York region had and incorporating harsh braking and harsh acceleration events as an independent variable. They could incorporate those variables in signalized and unsignalized intersections, for 4-legged and 3 legged intersections, and with the categories of all collisions and fatal injury collisions.

Predicted Harsh Braking and Harsh Acceleration Events at Signalized Intersections

CIMA+ developed separate models to predict CV events for the same categories where CV event was the dependent variable and AADT and some other road characteristics were dependent variables.  They developed similar models for similar categories for intersections and mid block locations. Using the developed models for harsh braking, they calculated the frequency of harsh braking across the regional roads displayed by the heat map below. They were also able to do this for harsh acceleration.

Conclusion

To conclude his presentation, Soroush shared a summary of his findings from leveraging AirSage CV data in his team’s safety analyses:

“Connected vehicle data provided us with the opportunity to create a fourth list of priority locations and we used that information in the development of the action plan for the York region to identify locations that certain counter measures could be incorporated.” – Soroush Salek, Director of Traffic Engineering, CIMA+

Next Steps

To watch the full webinar Enhancing Safety Studies With Vehicle Insight, please go to: https://airsage.com/webinars/. If you have further questions or want more information about CV Event (Safety Events) Data, contact AirSage at transportation@airsage.com.

 

LBS Data is Not Dead (Part 2): A Study of Actual Traffic Events Using AirSage Location-Based Services Data

Introduction:
In our previous article LBS Data is Not Dead: The Changing Landscape of Location Based Services Data for Transportation Planning, we acknowledged the concerns in the transportation planning industry regarding the value of location-based services (LBS) data. Data providers exited the market, causing a general decrease in data availability. However, despite these changes, LBS data remains a highly valuable resource for various use cases, with AirSage continuing to provide high-quality, accurate and reliable data.

Visualizations:
To illustrate the high level of accuracy and quality of AirSage's LBS data, let's examine visualizations of AirSage’s LBS data at three locations included in a recent evaluation:

1.) I-95 in Philadelphia, Pennsylvania: The following images present LBS datapoints (i.e., mobile device pings) in the vicinity of a catastrophic event wherein an entire segment of I-95’s both northbound and southbound was destroyed in a crash involving a tractor trailer. The left image presents typical conditions and the right image presents conditions during the full roadway closure.

   

As expected, the area of the roadway closures on I-95 during the roadway/bridge closure was clear of all LBS sightings.

2.) I-15 in Las Vegas, Nevada: The following images present LBS datapoints in the vicinity of a crash involving a pedestrian on I-15. The left image presents typical conditions and the right image presents conditions during the closure of all southbound travel lanes.

   

During the closure of all southbound travel lanes on I-15, the southbound lanes were clear of all LBS sightings, while the northbound travel lanes still provided LBS sightings, aligning with expectations.

3.) I-90 in Schaumburg, Illinois: The following images present LBS datapoints in the vicinity of a crash involving multiple vehicles on I-90, northwest of Chicago. The left image presents typical conditions and the right image presents conditions during the closure of eastbound travel lanes, with shoulder use observed.

     

The eastbound travel lanes were heavily congested but not completely closed due to the crash event (shoulder use was observed for a number of vehicles traversing through the scene). LBS sightings along the eastbound lanes were concentrated west of the crash site, as anticipated.

Why is AirSage Data Accurate?

Before agreeing to leverage location data from any data provider, AirSage conducts a comprehensive vetting process. In addition to this qualitative review, AirSage also ensures that each potential data provider complies with strict privacy regulations and meets the highest standards of quality. Although competing organizations have access to location data from similar data providers, AirSage distinguishes itself through its data sourcing, cleansing, and processing approach, in addition to its technical expertise. The AirSage team cleanses the data to the highest extent possible, removing instances of data that is unsuitable for transportation planning purposes.

Conclusion:

Based on the evaluations conducted for the three event locations, and the resulting visuals that present AirSage’s LBS data, it is evident that AirSage's LBS data accurately captures movement activities. The examples reinforce the high quality and level of accuracy provided by AirSage's LBS data. Transportation professionals can confidently rely on the insights derived from AirSage’s LBS data to make better-informed decisions.

AirSage's LBS data enables transportation professionals to enhance their understanding of human movement, identify home and work locations, infer trip purposes, and gain insights into various modes of transportation used for a trip. As the landscape of location-based services data continues to evolve, AirSage remains committed to delivering reliable, high-quality, and accurate mobility data for the transportation planning industry.

To learn more about AirSage LBS  data, visit AirSage's website.

 

About the Author:

Jonathan Silverberg, CTO & Co-President of AirSage has over 22 years of experience in senior technology management. He is the former CEO at Decell, a global leader of real-time traffic information specializing in leveraging mobile signaling data as well as GPS data for transportation applications. He is also the inventor of several granted patents in the fields of mobile communication and traffic information.

 

Introduction:

Transportation planning is a complex undertaking that requires accurate and reliable data to make the most informed decisions. AirSage, with its industry-leading mobility data, is often approached to explain why their data should be used for travel demand models and other transportation projects. Additionally, the debate between using modeled output or output derived from a synthetic population for a travel demand model arises frequently. In this blog post, we will explore why it is essential to understand the nuances of the inputs in your travel demand model and why black box output is not an appropriate choice for planning.

The Importance of Knowing the Ingredients in Your Data:

As transportation professionals, we understand that details matter. To achieve accurate forecasts for transportation projects, it's crucial to base your model on the behaviors of the actual population rather than relying on someone else's simulated data. Undoubtedly, different people represented in your model do different things, and that's why leveraging a large sample size is indispensable. Using modeled output can only offer insights based on specific potential scenarios and assumptions, such as average vehicle occupancies or predispositions towards carpooling or using alternative modes of transportation. 

The Pitfalls of Black Box Output:

Although other mobility data providers may produce comprehensive outputs, it is not recommended to leverage output from one black box as an input for another black box. The challenge lies in explaining the assumptions behind these outputs to constituents or a Council. Without understanding the underlying assumptions, it is challenging, if not impossible, to draw conclusions about what motivates people to choose specific modes of transportation and when to make a trip.

Unveiling the Unique Characteristics of Different Communities:

We know that residents of different neighborhoods have varying tendencies to use non-auto modes of transportation, and certain people make trips at specific times or days compared to others. These intricate details get lost when relying on modeled output as an input for understanding how people conduct trips in your travel demand model. By blindly accepting these outputs as ground-truth, we fail to acknowledge that the output is merely various representations of what someone else perceives as reality. Moreover, the people that produce these outputs often lack the localized knowledge that transportation planners and consultants possess, which hinders the output accuracy.

The Need to Show Our Math:

Transportation planners have a responsibility to credibly demonstrate transparency and confidence in the data and conclusions they present. In public hearings, it is necessary to explain the elements that went into the planning process before drawing conclusions and making recommendations. Responding with, "this is what the output says" is insufficient. Professionals must delve into the nuances of the details and methodologies behind their conclusions, valuing datasets differently and providing a higher level of confidence in their recommendations. After all, recommendations for transportation improvements are costly and rely on tax-payer dollars.

Conclusion:

When it comes to transportation planning, understanding your data is paramount. AirSage's industry-leading mobility data offers the opportunity to understand transportation behaviors and patterns accurately, enabling better informed decision-making. Avoiding black box output and synthetic populations ensures that your travel demand model incorporates the unique characteristics of your actual population. By "showing your math" and explaining the intricacies of the planning process, transportation professionals can provide reliable recommendations based on real-world insights. Choose transparency, accuracy, and confidence by using AirSage data for your transportation projects.

Introduction:

Transportation safety planning is critical for all entities tasked with the design, operations, and maintenance of roadways. As transportation network connectivity continues to improve, and new datasets become more available, evaluation metrics have evolved and permit planners and engineers to go further than ever before – thus having a significantly greater impact on improving safety for all roadway users. With AirSage’s mobility data offerings, organizations can improve their safety planning capabilities, enhance reliability of their analyses, and achieve their safety goals.

Benefits of Incorporating AirSage Data into Safety Planning
There are many benefits of utilizing AirSage’s industry leading data in safety planning.

  1. Improve Predictability of Safety Performance Functions (SPFs)
    AirSage’s expertise in data sourcing allows them to provide the most appropriate mobility data for identifying specific locations to propose road safety projects. Using AirSage data improves the predictability of SPFs and helps meet safety goals.
  2. Location-Based Safety Insights
    AirSage provides historical and near real-time insights on areas where road design changes should be considered. This helps highlight crash-prone locations so transportation planners can propose specific countermeasures for consideration.
  3. Enhance Behavior-Based Management
    AirSage data reveal locations with high concentrations of short-distance trips that could be conducted by walking, biking, or other non-auto modes.
  4. Ensure Inclusivity and Representativeness
    AirSage’s location-based services (LBS) data can be used to identify locations of trips that are conducted by members of underserved communities, helping to foster inclusive and representative safety planning development processes

Case Study: How CIMA+ Augmented a Regional Safety Plan
Soroush Salek, Director of Traffic Engineering at CIMA+ states: "AirSage connected vehicle data provided us with an opportunity to create a fourth list of priority locations, and we used that information in the development of the action plan to identify locations where certain countermeasures could be incorporated."

Conclusion
In conclusion, transportation safety planning is an essential element for entities responsible for the management and design of transportation networks. Conventional planning methods are becoming outdated, and new datasets that can underpin these studies are readily available. AirSage provides clients with the necessary insights required to significantly improve their transportation safety planning work. By utilizing connected vehicle and location-based services data, AirSage’s clients can identify crash-prone areas, assess traffic patterns in near real-time, and improve their decision-making capabilities with accurate data and insights.

To learn more about AirSage's data solutions for safety planning, visit their website here.

Written by: Jonathan Silverberg

Transportation professionals widely recognize the value of location-based services (LBS) data for various purposes, such as supplementing or replacing expensive and onerous household travel surveys. However, concerns have emerged in recent years regarding the data's ability to support certain use case applications.

Reduction in Data Availability Has Caused Concerns

Specifically, the ability to derive speed, mode, or full trip trajectories from mobile device data has come under question due to changes in data availability. By data availability, we mean the number of times each device generates a GPS sighting throughout the day, and, more importantly, the temporal difference between adjacent pings.

LBS Data Continues to Have Applications in Transportation Planning

Despite this reduction in data availability, LBS data still has legitimate applications, particularly for generating origin-destination trip tables (i.e., trip matrices). It continues to provide valuable insights into flows of people, identification of home and work locations, and inference of trip purposes. Additionally, LBS is the only source that provides insights, not only on the movement of people in cars but also as pedestrians, bikers, in transit, rail and air.

How AirSage’s LBS Data Panel Compares

Now, let's delve into the topic of LBS data availability and explore how AirSage's data panel fits into this context. AirSage evaluates its raw location data based on three key criteria: device quality, source reliability, and panel size (the quantity of high-quality reliable devices).

Based on these criteria, we find that AirSage's data panel today compares to its panel in 2019, which represents approximately 50% of its peak in early 2022. However, the growth rate of data availability remains strong, with an annual increase of approximately 33%. We anticipate that AirSage's panel will return to 2022 levels by the end of 2024. The only capability currently missing, compared to early 2022, is that which is derived from ultra high device reporting rates - mainly, the ability to infer mode of transport.

Conclusion

In conclusion, while there have been shifts in LBS data availability, it remains a highly valuable resource for transportation professionals, with AirSage continuing to provide high-quality, reliable data that is used to make better informed decisions.

If you’d like to discuss LBS data further, schedule a call with us.

 

About the Author:

Jonathan Silverberg, CTO & Co-President of AirSage has over 22 years of experience in senior technology management. He is the former CEO at Decell, a global leader of real-time traffic information specializing in leveraging mobile signaling data as well as GPS data for transportation applications. He is also the inventor of several granted patents in the fields of mobile communication and traffic information.

 

 

 

When it comes to air travel, there are countless airport code combinations representing various flight routes, such as JFK-LAX and LAX-SFO. But for transportation planners aiming to enhance landside operations at airports, there's a bigger question - “where are these people actually traveling between?”. Passengers flying from JFK to LAX certainly don’t live at John F. Kennedy International Airport in Queens (New York City), nor are they destined for a week of fun or meetings at Los Angeles International Airport in Los Angeles. Journeys involving air travel almost never actually begin or end at the airport. The trip might begin at home, an office, a house of a friend or relative, or even a hotel or convention center. Likewise, the journey is more likely to end at a resort, campus, office, or back at the traveler’s own home.

Landside Airport Planning Has Evolved Over The Years

Landside airport planning has evolved greatly over the years. It’s no longer just about drop-off and pickup of passengers. Planners now focus on a host of transportation options that leverage the complete transportation networks surrounding airports. These options include personal automobiles, private drivers (i.e., taxis, TNCs, shuttles, and limousines), public transportation, and even aerial service offered from niche aerial transport providers.

Travel Patterns of Outbound Passengers and Inbound Vary

While outbound passengers typically arrive at an airport gradually, over the course of a few hours before their flight’s departure time, inbound passengers typically leave the airport over a shorter period of time. Therefore, the concentrations of outbound and inbound passengers vary greatly. It’s also important to understand the dynamic of inbound passengers that are flying through the airport, on a connecting flight destined somewhere else. In fact, many of these people have no impact on landside operations outside of the terminal through which they are traveling.

What Airport Planners Need to Understand

Understanding how people arrive at and leave the airport is vital for effective landside airport planning. Airport planners need to know if travelers drive themselves, get dropped off, use private transport services, or rely on public transit. Equally important is understanding where these passengers come from before reaching the airport and where they are headed after leaving the airport. AirSage's industry-leading location data can answer these critical questions.

AirSage Can Help

With AirSage, airport planners gain access to location-based services (LBS) data generated by millions of smartphones nationwide. This allows them to see the complete journey patterns of passengers, enabling better-informed decision-making and ultimately more efficient landside airport operations. To learn more about how AirSage can help optimize airport planning, visit www.airsage.com!

 

 

 

Population density refers to the number of people living in a specific area, and it is an important factor in urban planning, transportation, and public policy. By understanding population density patterns, businesses and government agencies can make data-driven decisions about where to allocate resources, how to design infrastructure, and how to improve the quality of life for residents.

Mobile location data can be a powerful tool for optimizing population density by providing information about how people move and interact in different areas. By analyzing this data, businesses and government agencies can gain insights into population density patterns, such as where people tend to gather and when, and can use this information to make more informed decisions about how to allocate resources and plan for the future.

In this article, we will explore some of the ways in which mobile location data can be leveraged to optimize population density, and the benefits that this can bring for businesses, government agencies, and residents alike.

What is mobile location data?

Mobile location data refers to the information collected from mobile devices that indicates their physical location at a specific point in time. This data is typically collected through GPS, WiFi, or cellular network signals, and can be used to track the movement of individuals or groups of people.

Mobile location data is collected through the use of sensors and other technologies built into mobile devices such as smartphones, tablets, and wearable devices. These sensors use GPS, WiFi, and cellular network signals to determine the device's location, and this information is then transmitted to servers that can store and analyze the data.

In addition to GPS, WiFi, and cellular network signals, mobile location data can also be collected through other sources such as Bluetooth beacons, which are small devices that emit a signal that can be detected by nearby mobile devices. This allows for more precise tracking of individuals in indoor or densely populated areas where GPS signals may be weak or unreliable.

The Benefits Of Using Mobile Location Data To Optimize Population Density

There are several benefits to using mobile location data to optimize population density, including:

Improved urban planning and design: Mobile location data can be used to gain insights into population density patterns, such as where people tend to gather and when, and how they move through different areas. By analyzing this data, urban planners and designers can gain a better understanding of how to allocate resources and plan for the future, such as where to build new housing, public spaces, and infrastructure, and how to design streets and transportation systems that better meet the needs of residents.

Improved public transportation: Mobile location data can be used to track where public transit riders go, which lets transportation agencies adjust routes and schedules based on demand. By looking at this data, agencies can figure out where transit services are most needed and make changes to their operations to meet those needs. This makes public transportation more efficient and reliable.

Improved emergency response times: Mobile location data can be used to track the movement of people during emergency situations, such as natural disasters or public safety incidents. By analyzing this data, emergency response teams can gain a better understanding of where people are located and how they are moving, allowing them to respond more quickly and effectively to emergencies.

The Challenges Of Using Mobile Location Data To Optimize Population Density

Even though there are many benefits to using mobile location data to optimize population density, there are also a number of issues that need to be dealt with. Two significant challenges include:

Data privacy concerns: The collection and use of mobile location data can raise privacy concerns, particularly when the data is collected and used without the explicit consent of the individual. As such, businesses and government agencies must use mobile location data that has been collected in a way that respects individuals' privacy rights. 

Data that is wrong or missing: Mobile location data can be wrong or missing, especially in places where GPS signals are weak or where WiFi or cellular networks aren't available. This is why businesses and agencies should look for a location intelligence partner, like AirSage, that has already cleansed and sorted the data to remove inaccuracies and rectify missing information. 

How Mobile Location Data Is Currently Being Used To Optimize Population Density

Cities and urban planners use a variety of tools, such as GPS tracking, WiFi and Bluetooth signals, and cellular tower data, to look at mobile location data. This data is then analyzed using geographic information systems (GIS) software and other data analysis tools to identify patterns and trends in how people move through the city. This can include identifying areas with high levels of foot traffic, areas that are congested with cars, or areas that are underutilized and could be repurposed for public space.

Other cities and urban planners are using mobile location data to improve public transportation, cut down on traffic jams, and speed up response times in emergencies. For example, in London, transportation officials are using mobile location data to track bus and train movements in real time and identify areas of the city where traffic congestion is causing delays. This data is then used to adjust traffic signals and reroute buses and trains to improve efficiency and reduce travel times.

Wrapping Up

In conclusion, using mobile location data to optimize population density has a lot of potential to improve urban planning and transportation systems in the future, but it also comes with a few problems that will need to be solved. We can make cities more sustainable by developing new technologies, using multiple sources of data, being open, and including everyone in the planning process. For more details on mobile location data, contact AirSage today! 

The transportation industry is an important part of the world economy because it makes it possible for people and goods to move over long distances. But the industry has been slow to adopt new technologies in the past, which has led to inefficiencies and missed chances for growth and innovation. Mobile location data is changing the transportation industry by showing how people act, making logistics and supply chain management better, and making transportation networks more efficient overall. Businesses in the transportation industry can use mobile location data to make data-driven decisions that are good for their bottom line and improve the customer experience. In this article, we will explore how mobile location data is transforming the transportation industry and why it is such an important asset for businesses in this sector.

Real-time traffic monitoring

Real-time traffic monitoring is the process of tracking traffic patterns and congestion in real-time by using mobile location data. By collecting and analyzing data from mobile devices, transportation companies can learn more about how traffic flows and make decisions based on the data to make their operations run more smoothly.

There are several benefits to real-time traffic monitoring in the transportation industry. First, it helps transportation companies improve their routes and schedules, which can cut down on delivery times, make them more efficient, and save money. Second, it lets transportation companies react to problems with traffic in real-time, so they can reroute drivers or change schedules as needed. Third, real-time traffic monitoring can help transportation companies find bottlenecks and places where traffic is backed up. This lets them improve traffic flow and make it less likely that accidents or delays will happen.

Predictive Analytics

Predictive analytics is a data analysis technique that uses historical data, statistical algorithms, and machine learning models to predict future outcomes. Predictive analytics can be used in the transportation industry to predict demand, find the best routes and schedules, and cut costs.

There are several benefits to using predictive analytics in the transportation industry. First, it can help transportation companies learn more about how people use their services, so they can change how they run their businesses to meet changing needs. Second, predictive analytics can help transportation companies find potential problems before they happen. This lets them take steps to prevent fraud and save money. Third, it can help transportation companies improve their supply chain management by letting them make decisions based on data about how to handle inventory, delivery schedules, and other things.

 Improved Logistics and Supply Chain Management

Improved logistics and supply chain management mean using mobile location data and other technologies to make the flow of goods and materials through the supply chain as efficient as possible. By collecting and analyzing data on inventory levels, transportation routes, and customer demand, transportation companies can make data-driven decisions that improve the efficiency and effectiveness of their supply chain management.

Improving logistics and supply chain management in the transportation industry can help in a number of ways. First, it enables transportation companies to better manage inventory levels, ensuring that they have the right products in the right place at the right time. Second, it lets transportation companies figure out the best routes, which cuts down on delivery times and costs. Third, it can help transportation companies find ways to improve their processes and make them more efficient. This can help them improve their business and cut costs.

Privacy Concerns and Regulations

Privacy concerns are a major issue when it comes to the use of mobile location data in the transportation industry. Mobile location data can be very sensitive because it can show where and how people move and what they do. As a result, there is a risk that the use of mobile location data in transportation could compromise individuals' privacy rights.

To address these concerns, there are several regulations and laws in place that govern the use of mobile location data. In the European Union, for example, the General Data Protection Regulation (GDPR) says that companies must get permission from people before collecting and using their personal information, such as their mobile location data. Additionally, the California Consumer Privacy Act (CCPA) in the United States requires companies to provide consumers with the ability to opt out of the sale of their personal information, including mobile location data.

This is why companies should partner with a location intelligence solution, like AirSage, that follows all relevant privacy regulations and ensures the data is correct, reliable, and safe.

Wrapping Up

In conclusion, mobile location data is changing the transportation industry by making it possible to track traffic in real-time, use predictive analytics, and run logistics and the supply chain more efficiently. Because of these improvements, transportation companies can now make decisions based on data that improve efficiency, cut costs, and make the customer experience better.

Looking to the future, there is a lot of room for mobile location data to be used in transportation in new ways. For example, advancements in machine learning and artificial intelligence could enable even more sophisticated predictive analytics and optimization, while new technologies such as connected and autonomous vehicles could transform the way we think about transportation.

Overall, the use of mobile location data in the transportation industry is transforming the way we think about transportation, and the future is bright for continued innovation and improvement in this space. For more details about mobile location data and its applications in transportation, visit AirSage today!

Understanding population trends is critical for businesses to make data-driven decisions. Population trends can provide valuable insights into consumer behavior, spending habits, and other essential factors. In recent years, activity density data availability has revolutionized how businesses can use population trends to make decisions.

Activity density data is a type of location data that tracks the number of people in a specific location. This data can be collected through various sources, including mobile devices, and other location-enabled technologies. By analyzing activity density data, businesses can gain insights into the movement of customers such as where people go, when they go there, and how long they stay.

Using activity density data has opened up new opportunities for businesses to understand their customers better and make data-driven decisions. By leveraging this type of location data, businesses can gain a competitive edge in today's market and drive growth and success in the years to come.

Collection of Activity Density Data

AirSage is a leading provider of activity density data and uses a unique approach to collect and analyze location data. The company uses tools and software to collect anonymous location data from various sources, including cellular networks, GPS devices, and other location-enabled technologies. AirSage's data is based on the signals emitted by mobile devices, which are anonymized and aggregated to provide insights into population density and movement.

AirSage's data sources include mobile app providers and other location data aggregators. By working with multiple data sources, AirSage can provide a more accurate view of population density and movement.

Understanding Population Trends through Activity Density Data

Activity density data refers to the information collected about the frequency and location of mobile devices within a particular area, such as smartphones and tablets. This data provides valuable insights into population trends, urbanization, migration patterns, and transportation. This section will discuss how activity density data can be used to understand these trends and inform infrastructure planning.

Activity density data can reveal unique trends by providing insights into the movement of people within a particular area. By analyzing the density and location of mobile devices, researchers can determine which areas are heavily trafficked This information can help businesses and city planners identify areas with high foot traffic, such as famous shopping districts. 

AirSage's data can also provide unique insights into urbanization and migration patterns. By tracking the movement of mobile devices over time, AirSage can determine which areas are experiencing growth and which are experiencing a decline. This information can predict future population trends and inform infrastructure planning.

Activity density data can be used to understand transportation patterns by identifying areas where people are travelling to and from.. By analyzing this data, city planners can identify areas where transportation infrastructure is needed and determine the best transportation mode, such as buses or trains.

In addition to transportation planning, activity density data can inform infrastructure planning in other areas, such as housing and commercial development. By identifying areas with high foot traffic, city planners can determine where to build new housing developments and commercial buildings and where to allocate resources to maintain and improve existing infrastructure.

Applications of Activity Density Data

Activity density data is a powerful tool for various applications, from urban planning and development to transportation planning and traffic management. In this section, we will discuss some of the critical applications of activity density data and the benefits that it can provide.

Activity density data can inform urban planning and development by providing insights into the movement of people within a particular area. By analyzing the density and location of mobile devices, city planners and developers can identify areas with high foot traffic and determine where to allocate resources to construct new buildings, infrastructure, and public spaces.

Activity density data can also be used in transportation planning and traffic management. By analyzing the movement of people within a particular area, city planners and transportation authorities can identify areas of congestion and determine the best strategies for reducing traffic and improving mobility.

Activity density data can also be used to predict and respond to population changes and emergencies. By tracking the movement of people within a particular area, city planners and emergency responders can identify areas with a high concentration of people and determine the best strategies for responding to emergencies or providing resources during times of crisis.

Challenges and Future Directions

While activity density data provides valuable insights into population trends and movement patterns, several challenges are associated with its use.

Privacy concerns are among the most significant challenges associated with activity density data. The data is typically collected from mobile devices, which can raise concerns about collecting and using personal data. There are also concerns about the potential misuse of the data, such as tracking the movement of individuals for malicious purposes.

To address these concerns, it is essential to develop data privacy policies and security measures to protect the personal data of individuals. It is also vital to ensure that data collection and analysis are conducted transparently and responsibly.

Another challenge associated with activity density data is the limitations in data accuracy. The data is typically collected from a subset of the population, which can lead to biases and inaccuracies in the analysis. Additionally, the accuracy of the data can be affected by factors such as poor signal strength or device battery life.

To address these limitations, it is vital to develop methods for improving the accuracy of the data, such as combining data from multiple sources or using advanced algorithms to correct for biases and inaccuracies.

Despite these challenges, several potential future directions exist for activity density data analysis.

Machine learning algorithms can analyze large volumes of activity density data and identify patterns and trends that may not be immediately apparent to human analysts. This can help to improve the accuracy and efficiency of data analysis and enable more informed decision-making.

Advanced data visualization techniques can be used to present activity density data in a more accessible and engaging format. This can help to communicate the insights and findings of the analysis to a broader audience and enable more effective collaboration between different stakeholders.

Activity density data can be integrated with other data sources, such as social media or weather data, to understand population trends and movement patterns comprehensively. This can help to identify the underlying factors that drive population movement and enable more effective planning and decision-making.

Wrapping Up

In conclusion, activity density data is essential for understanding urban population trends and movement patterns. By analyzing the movement of people through mobile devices, we can gain valuable insights into the factors that drive population movement and inform more effective urban planning and development. 

AirSage's approach to activity density data analysis offers several unique features and benefits, including high levels of accuracy, daily data output, and a robust data privacy policy that is fully compliant with required regulations (CCPA/CPRA and GDPR). As we continue to explore new methods for data analysis and visualization, activity density data has the potential to shape the future of urban planning and development, enabling more sustainable and efficient cities that better meet the needs of their inhabitants.

Location intelligence is a powerful tool for businesses to gain insights into their operations and customers. It involves collecting and analyzing location-based data to identify patterns and trends. This type of intelligence is used in various industries, from retail to public services. When it comes to customer understanding, location intelligence is essential. 

Overall, location intelligence is crucial for businesses looking to improve their operations and better understand their customers. Businesses can better understand their behavior and preferences by gathering and analyzing customer location data. This knowledge can be used to create targeted marketing campaigns, improve customer engagement, and increase revenue. 

Collecting Location Data

To harness the power of location intelligence, it's vital to collect accurate and reliable location data. There are several sources of location data and various methods for collecting it.

  1. a) GPS - Global Positioning System, is a technology that uses satellite signals to determine a device's location.
  2. b) Mobile Apps - Many apps collect location data through GPS, WiFi, or cell tower triangulation.
  3. c) IoT sensors - Internet of Things (IoT) sensors can collect location data from devices such as wearables, vehicles, or even traffic lights.
  4. d) Social Media - Social media platforms like Twitter, Facebook, and Instagram can provide location data when users post content.
  5. e) Geofencing - A geofence is a virtual perimeter around a specific location that triggers an action, such as sending a notification, when a device enters or leaves the area.

Location data quality and accuracy can vary depending on the source and method of collection. GPS data, for example, can be highly accurate, while WiFi or cell tower triangulation may be less precise. Environmental factors such as buildings or other structures interfering with GPS signals can also affect data quality. 

It's essential to consider the quality and accuracy of location data when using it for business insights, as inaccurate or unreliable data can lead to incorrect conclusions and decisions. Additionally, privacy concerns should be considered when collecting and using location data, and appropriate measures should be taken to protect users' privacy.

 Analyzing Location Data

Analyzing location data is a crucial part of location intelligence. Several types of analysis can be performed on location data, as well as visualization techniques and predictive modeling that can be used to gain insights.

  1. a) Spatial analysis involves analyzing the spatial relationships between different locations, including distance calculations, buffering, and clustering.
  2. b) Temporal analysis - This involves analyzing the temporal patterns of location data, such as changes in the frequency of visits to a particular location over time.
  3. c) Network analysis - This involves analyzing the connections between different locations, such as traffic flow or the movement of goods between different locations.
  4. d) Demographic analysis - This involves analyzing location data in combination with demographic data, such as age, income, and education level.
  1. a) Heat maps - These maps show the intensity of location data in a particular area, often represented as a color gradient.
  2. b) Cluster maps - These maps show group location data points close together, indicating high-density areas.
  3. c) Flow maps - These maps show the movement of people, goods, or other elements between different locations.

Location data can be used to create predictive models that can forecast future trends and behavior. For example, a business may use location data to predict future foot traffic to a particular location or to forecast demand for a particular product or service. Machine learning algorithms can be trained using location data to make these predictions.

Analyzing location data is a powerful way to gain insights into business operations, customer behavior, and other relevant factors. Using visualization techniques and predictive modeling, businesses can leverage location data to make informed decisions that drive growth and increase revenue.

Using Location Intelligence for Customer Understanding

Location intelligence can provide valuable insights into customer behavior, preferences, and needs. Businesses can better understand their customers by analyzing location data and using this knowledge to create more personalized experiences.

Location data can provide insights into where customers go, how often they go there, and how long they stay. This information can be used to understand customer behavior and preferences, as well as to identify potential areas of growth. For example, a retailer may use location data to understand which stores are most popular among customers and which locations have the highest foot traffic.

Location data can also provide insights into customer profiles. For example, AirSage pairs census demographics with location data to help companies get a full picture of their customers. A restaurant may use this data to identify the gender, age, and economic class of the customers that enter their doors. 

Location data can also be used to create more personalized experiences for customers. For example, a retailer may use location data to send targeted promotions to customers near a particular store. This type of personalized messaging can increase customer engagement and loyalty.

Overall, location intelligence is a powerful tool for understanding your customer. Using location data to create more personalized experiences, businesses can increase customer engagement and revenue and gain a competitive edge in today's market.

Conclusion

Location intelligence has become an increasingly important tool for businesses to understand their customers better and make data-driven decisions. By collecting and analyzing location data, businesses can gain insights into customer behavior, preferences, and needs and use this knowledge to create more personalized experiences that drive growth and increase revenue.

Overall, location intelligence has the potential to revolutionize the way businesses understand and engage with their customers. By leveraging the power of location data, businesses can gain a competitive edge in today's market and drive growth and success in the years to come. To learn more about location intelligence, visit AirSage today! 

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