The convergence of vehicle and infrastructure data for traffic and demand management

Stanley Young
National Renewable Energy Laboratory

The increasing availability of highly granular, vehicle trajectory data combined with ever increasing stores of roadway sensor data has provided un-paralleled observability into the operation of our urban roadway networks. These data sources are quickly moving from research and prototype environments into full-scale commercial deployment and data offerings. The observability gained allows for increased control opportunities to enhance transportation mobility, safety and energy efficiency. The national renewable energy laboratory (NREL) is involved in three initiatives to leverage these data for positive outcomes.
• In 2015 NREL, in cooperation with industry and university partners, was awarded an ARPA-E research grant to research a control architecture to incentivize individual travelers toward more sustainable travel behavior. Based on real-time data on the traveler’s destination and state of the system, the traveler is presented with route and/or mode choices and offered incentives to accept sustainable alternatives over less-sustainable ones. The project tests the extent to which small incentives can influence, or tip the balance toward more sustainable travel behavior.
• Although commercial sources of travel time and speed have emerged in recent years based on vehicle probe data, volume estimates continue to rely primarily on historical count data factored for the time of day, day of week, and season of year. Real-time volume flows would enable better tools, simulation in the loop, and ultimately more effective control outcomes. NREL in cooperation with the University of Maryland and industry traffic data providers, are attempting to accelerate the timeframe to a viable real-time vehicle volume data feed based on probe data.
• Signal control on urban arterials for years has had to rely on models rather than measured data to assess performance. High-resolution controller data and low-cost re-identification data now allows for direct measurement of the quality of progression along a corridor. Though still requiring an investment in equipment and communications, these data sources are transforming traffic signal management to a data driven, performance management basis. Ever increasing availability of granular GPS trace data from automobiles may allow for assessment of traffic signal performance, allowing for signal optimization while minimizing the investment in additional sensors and communication infrastructure.

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