UC Berkeley

During the 2010s decade, the increase of connectivity in the world led to the development of navigation applications that help vehicles to travel within the transportation network using real-time traffic information. Information-aware routing changed traffic patterns by spreading congestion in the network. Before information-aware routing, route choice was dictated by direction signs, themselves prescribed by city traffic plans. As a consequence, with these new routing behaviors, some traffic plans have been outdated.

From a game theoretical point of view, by providing vehicles with the fastest path to reach their destination, information-aware routing suggestions direct the state of traffic toward a Nash equilibrium. The gap between the state of a game and a Nash equilibrium can be measured with the average deviation incentive. Using the restricting path choice model, we show that the average deviation incentive monotonically decreases when information-aware routing behaviors increase.

On the ground, if information-aware routing behaviors might (or might not) increase the overall transportation efficiency, some local roads received more traffic than the one they are designed to sustain. In Los Angeles, CA, we measure a 3-fold increase in the flow of one I-210 off-ramp between 2014 and 2017. To our knowledge, this is likely a consequence of the rise of information-aware routing behaviors due to an increase in navigational-app usage. Travel time data shows the travel time equalization between the main Eastbound I-210 route between Pasadena, CA, and Azusa, CA, and some alternate routes using local roads. The travel time equalization expresses that the state of traffic is a Nash equilibrium, which demonstrates the presence of information-aware routing behaviors. However, the severity of information-aware routing cannot be quantitatively assessed globally due to the lack of available floating-car data (trajectory data). Qualitatively, many cities and neighborhoods reported negative externalities of cut-through traffic due to information-aware routing: Los Angeles, CA recorded several crashes on the steep Baxter Street, Leonia, NJ reported a fatality on the Ford Lee Road, and Fremont, CA faced the challenges of cut-through traffic on the Mission Boulevard. All around the world, cut-through traffic might induce higher travel times, delays, unreliable travel times for residents, noise, gas emissions, traffic accidents, decrease accessibility in affected neighborhoods, wrong directions, and infrastructure damage, among others. To mitigate these issues, many cities changed the design of their road network. Others used cap-and-trade techniques (e.g., access restriction for non-residents on the Ford Lee Road in Leonia, NJ), or Pigovian taxes (e.g., road pricing on Lombard Street in San Francisco, CA). The city of Fremont erected new stop signs, built speed bumps, and updated its traffic signal timing plans to decrease the Mission boulevard's attractivity.

To pick the best mitigation techniques to fight against cut-through traffic, we suggest using a digital twin of the city traffic. Traffic microsimulators can replicate, in the digital world, the movement of each vehicle within the road network. Because the challenges of running a traffic simulation are not apparent until one creates their own, we provide a blueprint to develop, calibrate and validate a traffic microsimulation. A traffic simulation of the Fremont, CA neighborhood affected by cut-through traffic due to information-aware routing is made open source, for anyone that would like to understand how information-aware routing might lead to cut-through traffic.

On the way, we realized that simulating the behaviors of each vehicle in the network is computationally expensive. Therefore, we proposed to cluster vehicles into a mean-field by developing a mean-field routing game. While macroscopic routing models already exist to estimate how, knowing the traffic demand, traffic evolves in the network, we envision that the mean-field routing game is the best tool to perform large-scale dynamic routing control. We also envision that large-scale dynamic routing control is enabled by navigational applications, with already existing features like eco-routing that have been launched in 2021 by Google Maps.