This study proposes an end-to-end traffic signal control approach that seamlessly integrates dynamic traffic state information and real-time signal control decisions into a unified framework. This framework establishes a mapping function between traffic detector patterns and optimal adaptive control policies. The adjustments in the adaptive signal plan are cycle-based and driven by real-time traffic detector data represented by binary strings. We rely on traffic detection data from first principles rather than on estimations of traffic arrivals or residual queues as external inputs. The adaptive signal control problem is formulated into a two-stage stochastic program, explicitly addressing control variables including cycle time, base green time, offsets, and adaptive policies. A gradient descent algorithm is developed, aiming to derive optimal adaptive policies tailored to detector patterns. To validate the framework, we initially test it on a simple junction to illustrate the mapping properties and the selection of critical detector patterns. Subsequently, we extend the study to a corridor of three connected intersections to demonstrate versatility of the framework, encompassing intricate traffic configurations such as mixed-lane and pocket lane setups. This investigation contributes new insights into urban traffic control and management through a novel adaptive signal control modelling approach.

This study proposes a novel framework for traffic parameters estimation at signalized intersections with partial vehicle trajectory data, namely the Iterative Partial Backpropagation Maximum Likelihood Estimation (IPB-MLE) framework. The framework utilizes analytical approximations of the Poisson distribution, which enables the joint likelihood of the observed trajectories to be continuous and differentiable, hence providing better convergence results. The traffic parameters, including the Poisson parameter for the number of incoming vehicles, the free pace mean and variance, and the residual queue length, are optimized by maximizing the joint likelihood using a gradient-based algorithm with the Adam optimizer. The Iterative Backpropagation (IB) approach utilized in this framework allows for flexibility in the problem formulation. As shown in the experiments, the IPB-MLE framework yields good accuracies and robustness in satisfactory computational times. Empirical experiments further show that at least two trajectories are needed for guaranteed convergence, which implies that data volume is also a critical criterion (apart from penetration rate) for accuracy. Estimations take on average 10 to 20 seconds, respectively, for unsaturated and saturated cycles at a 20% penetration rate, and the framework has a linear computational complexity with respect to the number of variables and data points, making it suitable for online or real-time estimation. Moreover, the IPB-MLE framework can be easily extended to more complex situations by incorporating simulation models or neural networks to approximate the travel time distributions. These features of the IPB-MLE framework offer it great potential for more complex and realistic situations.

This paper jointly optimizes the zoning, scheduling, and pricing of a zonal-based flexible bus service (ZBFBS). ZBFBS categorizes and groups the origin-destination (OD) pairs of ride requests by geographical zones to provide door-to-door transit services while considering dynamic stochastic elastic demand volume, stochastic ride request locations, and time window constraints. The integrated problem can be formulated in two ways, either by designing the bus service routing in response to the realized demand, referred to as the demand-responsive method, or by a stochastic programming approach that accounts for the demand distribution. The former first clusters the realized requests into zones through the k-means algorithm, and then solves the joint vehicle scheduling and passenger assignment problem. As for the latter, a bi-level framework is proposed: the upper level optimizes the zoning, and the lower level maximizes the profit by scheduling and pricing the ZBFBS service. For this purpose, hexagonal and rectangular zonings are considered, whose positions and dimensions are optimized by a line search algorithm, with a deterministic approximation approach to significantly reduce the solution time. This framework is applied to a scenario based on actual ride-hailing data in Chengdu, China. The results demonstrate the benefit of the bi-level framework in producing an optimal zonal design. Also, the proposed bi-level stochastic zonal design framework outperforms the demand-responsive method in a tight planning time.

Stochastic traffic demands and turning ratios are critical factors in coordinated signal control. However, existing studies ignore the spatial-temporal dependencies of traffic flows between adjacent intersections and signal cycles. Turning ratios are usually assumed to be deterministic. This study develops a two-stage stochastic programming model for two-way coordinated adaptive signal control under stochastic traffic demands and turning ratios. A hierarchical multi-objective function is developed for overflow management and operational efficiency under both over- and under-saturated traffic. The primary and secondary objective functions minimize residual queue lengths and average vehicle delays, respectively, which are formulated considering spatial-temporal dependencies for the coordinated traffic flow. In stage one, a base coordinated signal timing plan is optimized to maximize the expected performance under stochastic scenarios. In stage two, adaptive cycle lengths and green times are determined by setting the tolerance factor for the base green times to maintain the stable traffic flow. The concept of Phase Clearance Reliability (PCR) is extended to decouple the interaction between the two stages. The deterministic equivalent problem of the proposed model in one signal cycle is modified to optimize the base signal timing plan for serving the stochastic exogenous and endogenous traffic demands up to certain PCR values. A PCR-based gradient algorithm is designed for solutions. The experimental results demonstrate that the proposed model can significantly improve traffic operation compared to six benchmarks.

This study develops a cell-based two-stage stochastic program to address the dynamic, spatial and stochastic characteristics of traffic flow for arterial adaptive signal control. To capture demand uncertainty, we formulate the adaptive coordinated traffic signal control as a two-stage stochastic program. To capture dynamic and spatial features of traffic flow, Cell Transmission Model (CTM) is embedded in the two-stage formulation. We incorporate the concept of Phase Clearance Reliability (PCR) to decompose the original two-stage stochastic formulation into separable sub-problems, which greatly enhances solution efficiency. A gradient-based solution algorithm is developed to solve the problem. Numerical examples are constructed to investigate the importance of capturing (or ignoring) each of the dynamic, spatial and stochastic features for traffic control. The results show that failure to account for any of these three traffic flow features will incur a certain extent of delay performance degradation, especially for heavy traffic. Finally, this study validates the findings through VISSIM, with promising results for the newly developed stochastic formulation.

The papers in this special section are devoted to the topic of Big Data for intelligent vehicle systems. issue. The collection of papers included can be categorized in several ways. In terms of applications, the special section covers transit network design, taxi and bus operation, traffic flow and travel time prediction, dilemma zone management, and geometric design of roads. In terms of data sources, it covers both conventional sources of data such as bus, taxi, and individual vehicle trajectories, and also novel sources of data such as those based on the connected vehicle technology, roadside communication equipment, or onboard devices. In terms of techniques or methodologies developed for processing big data, this collection includes papers covering the data imputation technique based on the k-nearest neighbor method, data management infrastructure, the machine learning approach, and the graph-theory-based method.

This paper develops an adaptive approach for vehicle navigation in a stochastic network with real-time en route traffic information. This stochastic and adaptive approach is formulated as a probabilistic dynamic programming problem and is solved through a backward recursive procedure. The formulation, as a modeling framework, is designed to be able to incorporate various sources of information and real-time traffic states to improve routing quality. In this paper, we prove that the approach outperforms deterministic instantaneous shortest paths in a statistical sense. We also analyze the algorithm's computational efficiency. The results from numerical examples are included to illustrate the performance of the adaptive routing policy that was generated by the formulation.

In this paper, we deal with the traffic assignment problem solving a multiclass equilibrium problem. In particular, we focus our analysis on when the overtaking of vehicles is permitted. A new family of link travel time functions is presented, which allows us to reproduce the same asymptotic congestion behavior of several overtaking classes to mimic the fact that high congestion impedes overtaking and that all classes must have identical link travel times. This family is generated based on local linear convex combinations of travel time Bureau of Public Roads (BPR) functions. A nonlinear complementary problem (NCP), which does not require path enumeration, is used to solve the user-optimal traffic assignment. An example is used to show the proposed methods and techniques. In particular, a case in which cars and motorcycles share the network is analyzed under congested and uncongested conditions.

An important consideration for traffic signal control is that traffic arrivals are not deterministic. The effect of stochastic arrivals is mainly handled by introducing stochastic terms in delay formulas. Although convenient, this approach is somewhat indirect. Moreover, when the degree of saturation is high, the system becomes transient; it is questionable whether a static or time-invariant result in the form of a delay formula is applicable. In this paper, instead of relying on steady state or equilibrium probability measures, the transient effect is captured by analyzing the state of the system from cycle to cycle based on a probabilistic treatment of overflow in an event tree. This approach can be used to analyze an existing timing plan or to design a timing plan that satisfies a certain clearance reliability requirement. Some numerical results are included to demonstrate this approach.