Technology has changed individuals’ travel behavior and time-use in so many ways. As much as it offers variety of benefits to societies, it may add to social exclusion phenomena, since the need for travel is being replaced by a click of a button in cell-phone. People don’t feel the need to leave their home to […]
The transportation sector accounts for a massive portion of greenhouse gas emissions and air pollution, making the adoption of sustainable and low-emission alternatives crucial for mitigating climate change and improving air quality. Electric vehicles (EVs) have emerged as a promising solution, offering reduced emissions and lower operating costs compared to conventional internal combustion engine vehicles.
A growing number of advanced technologies — including AI, automation, robotics, spatial computing, quantum technologies, and more — have the potential to revolutionize future travel behavior and demand. However successfully navigating the intersection between travel, emerging technologies, and a changing society, will demand bold new ideas and insights. Building on and integrating with the work
Traditional methods for monitoring road conditions are fraught with challenges. Field inspections are labor-intensive and costly, aerial photography is subjective, and mobile measurement systems (MMS) require substantial investment in geospatial technology. In response to these limitations, there is a growing interest in leveraging advanced 3D scanning technologies, such as LiDAR and RGB-D scanners, in conjunction
Deep Learning with LiDAR Point Cloud Data for Automatic Roadway Health Monitoring Read More »
Despite the fact that our existing models are not up to the job of predicting travel behavior in today’s rapidly changing landscape, and despite considerable evidence that attitudes help us explain behavior more completely and more meaningfully, attitudes are nowhere to be found in practice-oriented travel demand forecasting models. Two main objections have been raised
Recent years have witnessed many efforts to use smartphones to collect travel data. Typical examples include the automatic collection of sensor data such as location, accelerometer, or microphone readings, and personalized recommendation/behavior modification by gamifying travel and providing incentives for particular mode choices or building route choice models for active transportation modes such as bicycling.
Walking for transportation, health, and pleasure is an essential part of people’s lives in most cities. Knowing where people linger, the destinations that attract them, and how those places are accessed could assist in optimizing business locations and providing better security. In addition, predicting and sharing congestion times and locations (perhaps in real-time as in
Currently, few municipal or regional authorities have access to the disaggregate freight activity data needed for planning, operational decision-making, freight externality evaluation (e.g. air pollution, collision risk), or equity analysis. Due to stakeholder privacy concerns, freight data are often aggregated by geography and/or commodity, limiting direct applicability of published data for local analysis. As a
COVID is a crisis that is unanticipated both in its occurrence and also its length of impact. In the early days, many office employers implemented work-from-home policies while retail businesses shuttered, leading to deserted downtowns across the country. Yet crisis is also an opportunity, and municipalities and businesses innovated in response to the fears of
The Effect of Urban Infrastructure Change on Movement Read More »
Technology has evolved at a tremendous pace over the past decade, permeating into our everyday existence and affecting literally every aspect of our lives. Our activity-travel choices have been no exception in this regard, as we make continuous and joint decisions about which activities we can and want to undertake (either in-person or virtually). Add
