NOx and CO Fluctuations in a Busy Street Canyon

Busy street canyons can have a large flow of vehicles and reduced air exchange and wind speeds at street level, exposing pedestrians to high pollutant concentrations. The airflow tended to move with vehicles along the canyon and the 1-s concentrations of NO, NO2 and CO were highly skewed close to the road and more normally distributed at sensors some metres above the road. The pollutants were more autocorrelated at these elevated sensors, suggesting a less variable concentration away from traffic in the areas of low turbulence. The kerbside concentrations also showed cyclic changes approximating nearby traffic signal timing. The cross-correlation between the concentration measurements suggested that the variation moved at vehicle speed along the canyon, but slower vertically. The concentrations of NOx and CO were slightly higher at wind speeds of under a metre per second. The local ozone concentrations had little effect on the proportion of NOx present as NO2. Pedestrians on the roadside would be unlikely to exceed the USEPA hourly guideline value for NO2 of 100 ppb. Across the campaign period, 100 individual minutes exceeded the guidelines, though the effect of short-term, high-concentration exposures is not well understood. Tram stops at the carriageway divider are places where longer exposures to higher levels of traffic-associated pollutants are possible.

Adaptive Optimization of Traffic Signal Timing via Deep Reinforcement Learning

With rapid development of the urbanization, how to improve the traffic lights efficiency has become an urgent issue. The traditional traffic light control is a method that calculates a series of corresponding timing parameters by optimizing the cycle length. However, fixing sequence and duration of traffic lights is inefficient for dynamic traffic flow regulation. In order to solve the above problem, this study proposes a traffic light timing optimization scheme based on deep reinforcement learning (DRL). In this scheme, the traffic lights can output an appropriate phase according to the traffic flow state of each direction at the intersection and dynamically adjust the phase length. Specifically, we first adopt Proximal Policy Optimization (PPO) to improve the convergence speed of the model. Then, we elaborate the design of state, action, and reward, with the vehicle state defined by Discrete Traffic State Encoding (DTSE) method. Finally, we conduct experiments on real traffic data via the traffic simulation platform SUMO. The results show that, compared to the traditional timing control, the proposed scheme can effectively reduce the waiting time of vehicles and queue length in various traffic flow modes.

Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “K,” which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimizing signals to reduce FC.

Hierarchical Longitudinal Control for Connected and Automated Vehicles in Mixed Traffic on a Signalized Arterial

This paper proposes a two-layer hierarchical longitudinal control approach that optimizes travel time and trajectories along multiple intersections on an arterial under mixed traffic of connected automated vehicles (CAV) and human-driven vehicles (HV). The upper layer optimizes the travel time in an optimization loop, and the lower layer formulates a longitudinal controller to optimize the movement of CAVs in each block of an urban arterial by applying optimal control. Four scenarios are considered for optimal control based on the physical constraints of vehicles and the relationship between estimated arrival times and traffic signal timing. In each scenario, the estimated minimized travel time is systematically obtained from the upper layer. As the results indicate, the proposed method significantly improves the mobility of the signalized corridor with mixed traffic by minimizing stops and smoothing trajectories, and the travel time reduction is up to 29.33% compared to the baseline when no control is applied.

Traffic Light Optimization Based on Modified Webster Function

Intersection traffic lights are a basic means of ensuring the normal operation of road traffic. A good signal timing scheme is essential for improving traffic congestion. To obtain the signal timing scheme of the designated intersection, the method proposed in this article is based on a modified Webster function. The method uses the signal cycle and proportion of green light duration as independent variables to establish the corresponding intersection vehicle delay function. This function is converted from a multiobjective optimization to a single-objective optimization formulation; a modified genetic algorithm is then used to find the optimal solution to this function. The experimental results show that the timing scheme optimized by the improved genetic algorithm can reduce the intersection delay by nearly 15.64%. The proposed traffic signal timing based on the modified Webster function will be of value as an important reference for the optimization of traffic lights at urban intersections.

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Queue length estimation is of great importance for measuring traffic signal performance and optimizing traffic signal timing plans. With the development of connected vehicle (CV) technology, using mobile CV data instead of fixed detector data to estimate queue length has become an important research topic. This study focuses on real-time queue length estimation for an isolated intersection with CV data. A Kalman filtering method is proposed to estimate the queue length in real time using traffic signal timing and real-time traffic flow parameters (i.e., saturated flow rate, traffic volume, and penetration rate), which are estimated using CV trajectories data. A simulation intersection was built and calibrated using field data to evaluate the performance of the proposed method and the benchmark method. Results show that when the CV penetration rate is at 30%, the average values of mean absolute errors, mean absolute percentage errors, and root mean square errors are just 1.6 vehicles, 20.9%, and 2.5 vehicles, respectively. The performance of the proposed model is also better than the benchmark method when the penetration rate of CVs is higher than 20%, which proves the validity of the proposed method. Furthermore, sensitivity analysis indicates that the proposed method requires a high penetration rate of at least 30%.