The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles’ lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads.

Reconstructing vehicle trajectories is an essential step in obtaining high-precision vehicle trajectory data owing to observation errors. Although considerable efforts have been made to reconstruct trajectories on road segments, it is still challenging to reconstruct two-dimensional vehicle trajectories at intersections. In this study, a three-step vehicle trajectory reconstruction method for intersections was proposed to correct abnormal velocities and direction angles in raw trajectories. First, outliers were identified by wavelet transform. They were then filtered out and interpolated using Gaussian kernel-based locally weighted linear regression. Finally, the trajectories were smoothed using a Savitzky-Golay filter. The proposed method was validated using empirical trajectory data collected at 15 intersections in Shanghai, China. The results demonstrate that the proposed trajectory reconstruction method performs well in terms of trajectory rationality and consistency. The mean outlier rate and root mean square error of the reconstructed trajectories are 3% and 0.821 m, respectively.

Accurately predicting road traffic flows is a primary challenge in the development of smart cities, providing a scientific basis and reference for urban planning, construction, and traffic management. Road traffic flow is influenced by various complex features, including temporal and weather conditions, which introduce challenges to traffic flow prediction. To enhance the accuracy of traffic flow prediction and improve the adaptability across different weather conditions, this study introduced a traffic flow prediction model with explicit consideration of weather factors including temperature, rainfall, air quality index, and wind speed. The proposed model utilized grey relational analysis (GRA) to transform weather data into weighted traffic flow data, expanded input variables into a new data matrix, and employed one-dimensional convolutional neural networks (CNNs) to extract valuable feature information from these input variables, as well as bidirectional long short-term memory (BiLSTM) to capture temporal dependencies within the time-series data. Bayesian optimization was employed to fine-tune the hyperparameters of the model, offering advantages such as fewer iterations, high efficiency, and fast speed. The performance of the proposed prediction model was validated using the traffic flow data collected at an intersection in China and on the M25 motorway in the United Kingdom. The results demonstrated the effectiveness of the proposed model, achieving improvements of at least 9.0% in MAE, 2.8% in RMSE, 2.3% in MAPE, and 0.06% in R² compared to five baseline models.

Nonrecurrent congestion on urban roads may cause overflow, possibly leading to gridlock at intersections. Signal control is one of the most important measures to prevent such phenomena. For nonrecurrent congestion with uncertain bottleneck capacity, however, existing signal control methods have mainly adopted the strategy of cutting off the green signal of the overflow movement while the release priority of green time has not been considered. Consequently, the green time allocated to overflow movements is too long, aggravating overflow and congestion. Therefore, a signal control method is proposed to prevent overflow caused by uncertain bottleneck capacity. The proposed method extends existing real-time adaptive signal control to explicitly consider the risk of queue spillover. When an overflow risk is detected, the green times of overflow movements will be reassigned using fuzzy control to determine the release priority depending on the queue length and red time. Numerical studies were conducted for two scenarios with different downstream bottleneck capacities. The results demonstrate that the proposed method not only outperforms existing methods in respect of intersection efficiency but also reduces the risk of overflow. The proposed method achieves a 27.7% to 33.8% reduction in the maximum queue length of overflow movements compared with a traditional adaptive signal control method and a 10.9% to 11.0% decrease compared with a modified signal control method with a cutting-off strategy. The sensitivity analysis indicates that the average delay resulting from the proposed method is 51.1% and 18.0% less than the delays resulting from the traditional and modified methods, respectively.

Freeway congestion usually occurs in on-ramp areas; however, it also occurs around off-ramp areas and can even spill back onto the main line, which is common on many congested urban freeways during peak hours. Nevertheless, traditional ramp metering strategies, one of the most successful approaches to reducing freeway congestion, have not considered the off-ramp traffic dynamics. Therefore, this study proposes a destination-aware coordinated ramp metering model for preventing off-ramp queue spillover and mainstream congestion. The model is established based on a model predictive control, which contains a traffic evolution module and an optimization module. The traffic evolution module is described in a destination-aware manner with explicit consideration of the impact on mainstream traffic due to off-ramp queue spillover. The optimization module especially takes into account the off-ramp queue constraints so as to prevent the spillover. A modified feasible direction algorithm is designed for solving the optimization problem. A comprehensive case study is conducted on a congested urban freeway network in Shanghai, China, during peak hours. A sensitivity analysis demonstrates its capabilities and robustness in preventing off-ramp queue spillover and freeway congestion. The results show that the proposed strategy reduces the total time spent by 8.3% with respect to the no-control case, outperforming the traditional ramp metering strategies without consideration of off-ramp traffic dynamics.

Travel time estimation plays an essential role in the high-granular traffic control and management of urban roads with distinct lane-changing conditions among lanes. However, little attention has been given to the estimation of distributions of travel times among different lanes and different vehicle types in addition to their expected values. This paper proposes a new method for estimating lane-based travel time distributions with consideration of different vehicle types through matching low-resolution vehicle video images taken by conventional traffic surveillance cameras. The vehicle type classification is based on vehicle sizes and deep learning features extracted by densely connected convolutional neural networks, and the vehicle re-identification is conducted through a lane-based bipartite graph matching technique. A case study is carried out on a congested urban road in Hong Kong. Results show that the proposed method performs well in estimating the lane-level travel time distributions by vehicle type which can be very helpful for various lane-based and vehicle type-specific traffic management schemes.