Traffic state estimation (TSE) lays the foundation for freeway traffic monitoring and control. This paper develops a novel freeway traffic state estimator based on physics-informed deep learning (PIDL), whereby the deep-learning neural network is guided with physical laws of traffic. The innovative features of this work are as follows. In the data-driven aspect, the representation capability of neural networks is enhanced by introducing a nonlinear expansion layer, an attention mechanism, and a point-weighting mechanism. In traffic flow modeling aspect, a neural network is designed for the adaptive identification of model parameters. Additionally, a ramp flow self-learning neural network is constructed in consideration of system observability. This study employs four macroscopic traffic flow models LWR, ARZ, JWZ and PW to design PIDL-based traffic state estimators. Field-data evaluations and comparative analyses are conducted with respect to an urban expressway of 17.5 km. The results show that the designed PIDL traffic state estimators are able to simultaneously achieve traffic state estimation, ramp flow estimation, and adaptive identification of model parameters. The PIDL estimators outperform a number of baseline estimators, and are least sensitive to the number of input sensors and missing data. It is also discovered that the effectiveness of PIDL hinges on the appropriate tuning of the imbedded physical model as well as accurate boundary condition inputs, and failure to meet these requirements may lead to performance degradation.

The connected automated vehicles (CAVs) are envisioned to be implemented most likely on electric vehicles, while traditional fuel-powered manually-driven vehicles (MVs) would probably still dominate the automobile market in the next decade. In this context, this paper addresses urban eco-driving of CAVs in mixed traffic and heterogeneous power conditions. The paper aims to develop a practical and deployable eco-driving strategy for CAVs in mixed traffic flow of CAVs and MVs under realistic and complex traffic conditions. Several typical eco-driving scenarios were studied in detail. In a nutshell, the eco-driving strategy for each CAV was determined by solving a typical two-point boundary value problem with minimum electric energy consumption in urban traffic conditions with small market penetration rates (MPRs) of CAVs. A rolling-horizon scheme was applied to implement the eco-driving strategy to handle uncertain/unpredictable disturbances of preceding MVs and the interference of junction queues to the eco-driving maneuvers of CAVs. The paper also studied how eco-driving for electrified CAVs would affect MVs’ fuel consumptions. Simulation studies were carried out on urban arterial roads of multiple signalized intersections in various scenarios of demand and MPR to verify the energy savings effect of the proposed eco-driving strategy. The results showed that via eco-driving electrified CAVs each had a potential of reducing energy consumption by 40%-61%, meanwhile leading to 5%-34% fuel savings on average for each following MV. Further issues concerning the energy saving mechanism of electrified CAVs, impacts of MVs cut-in from adjacent lanes, and passenger comfort were also examined.

Three filtering-based approaches to freeway traffic state estimation are studied using measurements from connected vehicles and also a minimum number of fixed detectors. These approaches are:Method 1based on EKF and the second-order traffic flow model METANET,Methods 2and 3 based on KF and the conservation equation that is driven by mean speed data of connected vehicles under a speed-uniformity assumption. Each method is capable of estimating segment traffic flow variables (speeds, densities, and flows) as well as segment market penetration rates (MPRs) of connected vehicles. The three methods are evaluated and compared in depth using NGSIM data with respect to their traffic state estimator design, data requirements, capabilities, limitations in the mixed sensing case. Recommendations are given about the choice of methods over the range of MPR.

In the context of Connected and Autonomous Vehicles (CAVs), this paper aims to examine the impacts of CAVs on mixed regular-automated traffic flow with the increase of the market penetration rate, in consideration of on-ramp merging and off-ramp diverging of vehicles. Lane changes are a major part of lateral motions, affecting surrounding vehicles locally and traffic flow collectively. On the basis of reinforcement learning technique, a cooperative lane-changing strategy was first developed to enable farsighted lane-changing behavior by CAVs in favor of traffic efficiency. The 3-lane highway stretch with one on-ramp and one off-ramp was applied in this study. With extensive simulations, the results suggest that the inclusion of CAVs considerably improves traffic flow, mean speed, and traffic capacity. Meanwhile, the existence of on/off-ramps has substantial impacts on the lane-changing processes. This work can shed some light on an aspect of the mixed traffic network dynamics for future mobility.

Capacity drop at congested freeway bottlenecks is well known with a lot of field observations. This paper studies coordinated ramp metering (RM) and mainstream traffic flow control (MTFC) as well as their integration (RM+MTFC) for freeway traffic, with particular attention to effects of capacity drop on the performance of traffic control measures. Via mathematical analysis and comprehensive simulation studies under an optimal control framework, the work has revealed a capacity-drop-related mechanism that MTFC and ramp metering are based on to take effects, and obtained a number of important conclusions: (1) applications of any control measure (RM, MTFC, or RM+MTFC) in freeways are justified by the existence of capacity drop in field; (2) an appropriate usage of the control measures can effectively prevent the activation of potential bottlenecks on freeways and hence avoid capacity drop; (3) any control measure is beneficial for a large majority of the driver population in a freeway network if it can manage to increase the accumulated total network exit flow; (4) it is a common misconception that ramp metering would simply transfer traffic loads from the freeway mainstream to on-ramps. The work has also highlighted the strengths, weaknesses, and applicability of ramp metering and MTFC.

Connectivity is a fundamental requirement for vehicular ad hoc networks (VANETs) to secure reliable information dissemination. Connectivity is not guaranteed in the case of traffic sparsity and low market penetration of networked vehicles. Therefore, it is essential to examine the connectivity condition before deploying VANETs. The probabilistic distribution of intervehicle spacing plays a crucial role in the study of connectivity. It is quite often in previous studies to assume a priori distribution. This paper has studied this issue analytically and proved a general result as follows. A Poisson vehicle flow of volume λ enters a road stretch over the period [0, ∞), with the speed of each vehicle sampled from a common probability distribution of the density function fV (v); then, in the steady state, the number of vehicles within any road section [x1, x2] at any time instant t > 0 is Poisson distributed with the parameter λ(x2- x1) f∞01/V f(v)dv. This theoretical result is also con1 0 firmed with extensive simulation studies.

Specific attention to vulnerable drivers makes crucial demands on adaptive design for driving support systems in the early stages of VANETs. This study aims to investigate the relative driving difficulties associated with diagnosed rheumatic diseases subjective symptoms and the use of some medications based on self-reported questionnaires. The survey offers a bridge between medical studies on physical impairments and driving safety for the specific driver group. To further understand the preference heterogeneity of vulnerable drivers, a latent class model for user sensitivity and perception on an adaptive drowsiness detection system was estimated.

Urban rail networks play an important role in urban transportation. An unexpected disruption in a rail network can cause a significant degradation in the level of service. When a disruption occurs, it is crucial to provide quick and efficient substitution of services via alternative transportation modes, including bridging disconnected railway stations using bus services. The amount of disruptions, surprisingly, is high; for example, there are more than 15 000 disruptions in six months in Melbourne, Australia. The provision of bus bridging services calls for proper planning and designing of a temporary bus bridging network considering limited bus and driver resources, and prevailing urban traffic conditions. Among a number of tasks concerning bus bridging, the demand modeling of affected train passengers is a prerequisite for a satisfactory bus bridging practice. This paper explores this demand modeling problem based on the theory of compound Poisson processes and formulates it as a bulk queuing problem involving balking and reneging. The problem is carefully studied, with a series of analytical results delivered. Large-scale Monte Carlo simulations were designed and implemented to demonstrate a range of mathematical conclusions.