Jorge Laval

Jorge A Laval, Guillaume Costeseque, Rama Chilukuri

This paper revisits the variational theory of traffic flow, now under the presence of continuum lateral inflows and outflows to the freeway say Eulerian source terms. It is found that a VT solution exists only in Eulerian coordinates when source terms are exogenous meaning that they only depend on time and space, but not when they are a function of traffic conditions, as per a merge model. In discrete time, however, these dependencies become exogenous, which allowed us to propose improved numerical solution methods. In Lagrangian and vehicle number-space coordinates, VT solutions may not exist even if source terms are exogenous.

Jorge Laval, Hao Zhou

This paper highlights several properties of large urban networks that can have an impact on machine learning methods applied to traffic signal control. In particular, we show that the average network flow tends to be independent of the signal control policy as density increases. This property, which so far has remained under the radar, implies that deep reinforcement learning (DRL) methods becomes ineffective when trained under congested conditions, and might explain DRL's limited success for traffic signal control. Our results apply to all possible grid networks thanks to a parametrization based on two network parameters: the ratio of the expected distance between consecutive traffic lights to the expected green time, and the turning probability at intersections. Networks with different parameters exhibit very different responses to traffic signal control. Notably, we found that no control (i.e. random policy) can be an effective control strategy for a surprisingly large family of networks. The impact of the turning probability turned out to be very significant both for baseline and for DRL policies. It also explains the loss of symmetry observed for these policies, which is not captured by existing theories that rely on corridor approximations without turns. Our findings also suggest that supervised learning methods have enormous potential as they require very little examples to produce excellent policies.

Hao Zhou, Jorge Laval, Anye Zhou et al.

Self-driving technology companies and the research community are accelerating their pace to use machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. We identify the availability of congestion scenarios in current datasets, and summarize the required features for training mMP. For learning methods, we survey the major methods in both imitation learning and non-imitation learning. We also highlight the emerging technologies adopted by some leading AV companies, e.g. Tesla, Waymo, and Comma.ai. We find that: i) the AV industry has been mostly focusing on the long tail problem related to safety and overlooked the impact on traffic congestion, ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and iii) albeit reinforcement learning (RL) approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning (BC), whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions towards congestion mitigation for future mMP studies are proposed: i) enrich data collection to facilitate the congestion learning, ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and iii) integrate domain knowledge from the traditional car following (CF) theory to improve the string stability of mMP.

Jorge A. Laval, Hao Zhou

This paper uses supervised learning, random search and deep reinforcement learning (DRL) methods to control large signalized intersection networks. The traffic model is Cellular Automaton rule 184, which has been shown to be a parameter-free representation of traffic flow, and is the most efficient implementation of the Kinematic Wave model with triangular fundamental diagram. We are interested in the steady-state performance of the system, both spatially and temporally: we consider a homogeneous grid network inscribed on a torus, which makes the network boundary-free, and drivers choose random routes. As a benchmark we use the longest-queue-first (LQF) greedy algorithm. We find that: (i) a policy trained with supervised learning with only two examples outperforms LQF, (ii) random search is able to generate near-optimal policies, (iii) the prevailing average network occupancy during training is the major determinant of the effectiveness of DRL policies. When trained under free-flow conditions one obtains DRL policies that are optimal for all traffic conditions, but this performance deteriorates as the occupancy during training increases. For occupancies > 75% during training, DRL policies perform very poorly for all traffic conditions, which means that DRL methods cannot learn under highly congested conditions. We conjecture that DRL's inability to learn under congestion might be explained by a property of urban networks found here, whereby even a very bad policy produces an intersection throughput higher than downstream capacity. This means that the actual throughput tends to be independent of the policy. Our findings imply that it is advisable for current DRL methods in the literature to discard any congested data when training, and that doing this will improve their performance under all traffic conditions.