Roadside sensors, such as camera and millimeter-wave (MMW) radar, provide traffic information beyond the visual range of intelligent vehicles in cooperative vehicle-infrastructure systems. Unlike ...onboard equipment, roadside sensors are affiliated with different systems and lack synchronization in both space and time. In this paper, we propose a novel spatio-temporal synchronization method of asynchronous roadside MMW radar-camera for sensor fusion, which utilizes features of the scenario to extract lane line corner points to pre-calibrate the camera. Based on the consistent time flow rate of the separate sensors, multiple virtual detection lines are set up to match the time headway of successive vehicles and conduct objective matching to track data. Finally, a synchronization optimization model is formulated and a constrained nonlinear minimization solver is applied to tune the parameters. Measure data from Donghai Bridge in Shanghai is applied to verify the feasibility and effectiveness of the method. The results determine that there are 33 frames (33*40 ms) of temporal deviation between the camera and the radar in this case. After the synchronization, the average spatial deviation is reduced from 2.47 m to 0.42 m in the X-direction and 64.06 m to 2.34 m in the Y-direction, respectively. This study provides an economical and effective way to solve the problem of spatio-temporal synchronization of roadside sensors.
A cooperative vehicle‐infrastructure system (CVIS) can provide perception information beyond the visual range for autonomous vehicles via roadside directional sensors, such as cameras, ...millimeter‐wave radar, and lidar. The performance of roadside perception strongly depends on sensor placement, where physical occlusion between vehicles is inevitable. This paper proposes an occlusion degree model (ODM) to describe the dynamic occlusion between sensors, obstacles, and targets in a three‐dimensional space. The ODM is then integrated with microscopic traffic simulation to study the impacts of traffic density, vehicle model composition, and sensor configurations on vehicle detection and tracking performances. Based on the simulation, a multifactor regression model with a logistic growth curve is established to quantify the performance of roadside sensors with different factors, where the evaluation metrics are rigorously designed. Finally, an optimization model of roadside sensor placement with non‐linear constraints is formulated to reduce the construction cost under a coverage constraint and improve the perception accuracy within budget limitations. A case study of a highway scenario shows that the performance of the proposed approach improves by 5.63%, 6.55%, and 8.01%, respectively, under the worst possible condition with accuracy requirements of 0.97, 0.96, and 0.95 compared with the conventional placement scheme. The study provides a map of the performance of roadside perception with different influencing factors and guides the optimal roadside sensor placement for a CVIS.
Millimetre‐wave (MMW) radars have been increasingly deployed along the roadside highways to collect vehicle trajectory data, which are valuable for traffic safety analyses and traffic control ...decisions. Nevertheless, possible errors of the trajectories resulting from roadside MMW radars have not been well documented. This study scrutinizes roadside MMW radar data and the concurrent video ground truth to identify five typical errors of vehicle trajectories, including different vehicles tagged with the same ID, different trajectories produced by the same vehicle, ghost vehicles, erroneous vehicle classification, and vehicle location drift. Data cleaning methods were developed to identify and correct these errors. The performance of the developed methods was evaluated empirically in a case study based on the data of five radars, which were installed at different locations along the highway and had different headings. The thresholds of the developed method were calibrated using one radar data and then applied to the data of the five radars. The results showed that the data qualities of all radars are greatly improved by the developed methods, demonstrating that the developed methods are generally applicable for cleaning vehicle trajectory data from roadside MMW radars.
The Modular Autonomous Vehicle (MAV) systems allow a vehicle module to join onto and detach from other modules to dynamically adjust vehicle capacity. It potentially renders transit agencies more ...flexibility to deal with the temporal fluctuations of passenger demand. In this work, we propose a strategy for flexible MAV scheduling on transit routes to meet the time-varying passenger demand. The proposed strategy is formulated as a bi-objective optimization model considering both the utilization of vehicles and service quality. The model determines the scheduled departure times from the terminals, the length of MAV for each scheduled trip, and the assignment of modules to all scheduled trips, simultaneously. The ε-constraint method is adopted to solve the developed model and the fuzzy satisfying approach is employed to select the best possible solution. We implement the proposed strategy in a real-world case study in comparison with a traditional strategy to demonstrate the effectiveness of the proposed strategy. The results show that the proposed strategy can remarkably improve the utilization of vehicles and also make passengers more convenient. Specifically, it leads to an 84.9% reduction in the total empty seat, as well as a 12.62% reduction in the total passenger waiting time.
Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal ...timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls.
This paper envisions a multimodal passenger-and-package sharing (PPS) network for urban logistics integrating metro, taxi, and truck. A hub-and-spoke structure is designed including hubs located at ...metro stations and service stores connected to the hubs. Packages are transported by metro on backbone links between the hubs and are carried by taxis or trucks between service stores and hubs, depending on the unit costs of these two modes and capacity constraint of the taxi. A mixed integer linear programming model for hub location problems—fusing the multiassignment p-hub median problem without capacity constraints and the capacitated multiassignment p-hub covering problem—is formulated to optimize the multimodal PPS network. The model is implemented based on the real-world data in Shanghai (China) under a series of scenarios to evaluate the network performance from two perspectives: the number of hubs and the proportion of taxi drivers who are willing to carry packages. The scenarios show that with increased number of hubs, the spatial distribution of hubs disperses from the city center to peripheral areas and more areas can be serviced by taxis. There is, however, a trade-off between the operation cost saved by taxis and the establishment cost of an extra hub. The analysis also presents that if the proportion of taxis willing to carry packages associates with the incentive payments to taxi drivers, an optimal value of incentives exists, by balancing the operation costs of taxis and trucks.
Electronic fareboxes integrated with Automatic Vehicle Location (AVL) systems can provide location-stamped records to infer passenger boarding at individual stops. However, bus loads and ...Origin-Destination (OD) flows, which are useful for route planning, design, and real-time controls, cannot be derived directly from farebox data. Recently, Wi-Fi sensors have been used to collect passenger OD flow information. But the data are insufficient to capture the variation of passenger demand across bus trips. In this study, we propose a hierarchical Bayesian model to estimate trip-level OD flow matrices and a period-level OD flow matrix using sampled OD flow data collected by Wi-Fi sensors and boarding data provided by fareboxes. Bus loads on each bus trip are derived directly from the estimated trip-level OD flow matrices. The proposed method is evaluated empirically on an operational bus route and the results demonstrate that it provides good and detailed transit route-level passenger demand information by combining farebox and Wi-Fi signal data.
This paper envisions and assesses the performance of an autonomous bus-on-demand (ABoD) system. We take Fuyang, Zhejiang, China, as the study area to investigate the spatiotemporal distribution of ...bus travel demand during workdays, and we propose replacing inefficient bus routes with the ABoD system. Agent-based models with various bus dispatching and operation control strategies are constructed to evaluate the performance of the ABoD system. The behaviors and interactions of the agents, passengers, autonomous buses, and a control center are designed. After the verification of the simulated bus travel demand with real-world demand, a series of scenarios with various ABoD operation strategies are simulated. The simulation results show that, in comparison with both current fixed-schedule bus services and the optimized bus dispatching strategies, the ABoD system occupies fewer road resources and utilizes bus vehicles more efficiently. Besides, the system is adaptive to the sudden surge in bus travel demand and is economically sustainable.
•Naturalistic observations of CB operation status before and after COVID-19.•Exploration of influential factors affecting the in-practice decision-making process.•Consistent findings from discrete ...choice models and machine learning models.
This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.
Real-time pedestrian volume data are becoming increasingly important to business strategy adjustment and guiding measures of shopping malls, tourist attractions, and transportation hubs. Wi-Fi probes ...are widely used to capture and study the media access control layer information of mobile devices but often with low detection and precision. This article mainly proposes an enhanced method to increase detection rate and precision under Wi-Fi-based system. Device test was first introduced to guarantee the performance of probes. Based on the theoretical analysis on the influences of probes’ relative locations, four layout schemes of multi-probes were compared, and the optimal one with the highest detection rate was identified experimentally. Based on the optimal layout, an estimation model between actual and detected volume was established after data cleaning. In total, two correction parameters were introduced to modify the model and shows a high estimation accuracy (root mean square error is 15.32 persons) in the experiment at Tongji University. The results of experiment proved that the proposed optimal layout scheme of multiple probes and estimation model can effectively improve the detection performance and precision of pedestrian volume and help increase the reliability and application value of Wi-Fi-based detection.
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