•Ride-sourcing drivers enjoy flexibility in deciding their work schedules.•This paper models the effect of surge pricing on drivers’ work scheduling.•Equilibrium models are formulated based on two ...hypotheses of labor supply.•The effect of surge pricing is investigated using a bi-level programming framework.•A simple regulation scheme to reduce market power is discussed.
This study proposes equilibrium models under different behavioral assumptions of labor supply in a ride-sourcing market and then investigates the performance of surge pricing. A time-expanded network is first proposed to delineate possible work schedules of drivers. Based on the proposed network, we provide formulations and algorithms for both neoclassical and income-targeting hypotheses to characterize the labor supply of ride-sourcing drivers, i.e., their choices of work hours. We then investigate the impact of surge pricing using a bi-level programming framework, with the lower-level problem capturing the equilibrium work hour choices while the upper-level one representing revenue-maximizing surge pricing. Compared to static pricing, the platform and drivers are found to generally enjoy higher revenue while customers may be made worse off during highly surged periods. Lastly, a simple regulation scheme to reduce market power is discussed.
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.
•Certain areas of road networks can be dedicated to autonomous vehicles (AVs) only.•AV zones enable full utilization of the AV technology and further nurture the AV market.•A mathematical framework ...is developed for the optimal design of AV zones.•A novel network equilibrium model is proposed to capture mixed-routing behaviors.
This paper advocates the need for infrastructure planning to adapt to and further promote the deployment of autonomous vehicle (AV) technology. It is envisioned that in the future government agencies will dedicate certain areas of road networks to AVs only to facilitate the formulation of vehicle platoons to improve throughput and hopefully improve the performance of the whole network. This paper aims to present a mathematical framework for the optimal design of AV zones in a general network. With the presence of AV zones, AVs may apply different routing principles outside of and within the AV zones. A novel network equilibrium model (we refer to it as the “mixed routing equilibrium model”) is thus firstly proposed to capture such mixed-routing behaviors. We then proceed to formulate a mixed-integer bi-level programming model to optimize the deployment plan of AV zones. Numerical examples are presented to demonstrate the performance of the proposed models.
Automated valet parking (AVP) has attracted much attention as the entry point to autonomous driving. In an indoor environment, high-precision positioning systems are essential for AVP. Ultra-wideband ...(UWB) is one of the most widely adopted techniques. However, the base station placement significantly influences the system’s positioning accuracy, especially for the irregular architecture of underground parking lots. This article proposes a three-stage practical and economical layout planning approach for UWB base stations, including determining the deployment strategy and layout parameters and comprehensive adjustment and scheme verification. The approach considers regional differentiation accuracy requirements for AVP, such as ramp area, surface fluctuation area, and narrow area. The adopted positioning method of a UWB system is the time difference of arrival (TDOA), and the evaluation index of positioning accuracy is the horizontal dilution of precision (HDOP). Through experimental tests in an actual parking lot, the proposed approach is confirmed to ensure stability and economy with fewer UWB base stations and can meet the positioning accuracy requirements of AVP.
Carbon-based materials, as electrodes for supercapacitors, have attracted tremendous attention. Therefore, nitrogen-doped porous carbons (NPCs) were prepared through a facile carbonization/activation ...strategy by treating different mass ratios of melamine–urea–formaldehyde resin and KOH. It is clearly demonstrated that because of the introduction of KOH, the resulting NPCs were shown to have increased specific surface area and a rich pore structure, and the best sample possessed a large specific surface area of 2248 m2 g–1 and high N content, which contributed to the good electrochemical performance for supercapacitors. Accordingly, a three-electrode system assembles NPCs as an electrode using aqueous KOH solution; the specific capacitance was 341 F g–1 under the current density of 1 A g–1 and retained a specific capacitance of almost 92% after 5000 cycles. The maximum energy output for a symmetrical solid-state supercapacitor with NPCs as the electrode material was 9.60 W h kg–1 at 1 A g–1. NPCs have promising applications on high-performance supercapacitors and other energy-storage devices.
Safe and efficient autonomous lane changing is a key step of connected automated vehicles (CAVs), which can greatly reduce the traffic accident rate and relieve the traffic pressure. Aiming at the ...requirements of the smoothness and efficiency of the lane-changing trajectory of CAVs, it is necessary to design the lane changing controller to integrate the sensing, decision-making, and control tasks in the driving process. Firstly, based on the vehicle dynamics model, this paper proposes a vehicle lane-changing control strategy based on NNTSMC method (neural network enhanced non-singular fast terminal sliding mode control). The designed lane-changing controller can well realize the designed path tracking, and both lateral position and yaw angle can well track the expected value. This method enables the vehicle to control the front wheel steering angle intelligently, and the lateral acceleration during steering changes in the small scope, which ensures the steering stability of the vehicle. In this study, an improved adaptive RBF neural network with bounded mapping is designed to estimate the upper bound of the total disturbance of the system, which effectively reduces the chattering phenomenon of the control force. The Lyapunov function constructed in this study proves that the designed controller can ensure the stability of the controlled system. Finally, a comparative experiment is performed by the MATLAB/Simulink-CarSim co-simulation. Compared with SMC and TSMC (non-singular fast terminal sliding mode control), the proposed method has a performance improvement of at least 58.0% and 34.1%, respectively. The effectiveness and superiority of the proposed control method were confirmed by the experiments on the co-simulation platform.
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.
Traffic crashes tend to be more serious when commercial trucks are involved. And an increasing number of commercial vehicles are equipped with advanced driver assistance systems (ADAS) for traffic ...safety improvement. To this end, this study investigated the impacts of ADAS with respect to forward collision warning (FCW), urban forward collision warning (UFCW), lane departure warning (LDW), headway monitoring and warning (HMW), and speed limit indicator (SLI) on commercial truck drivers' behaviours using naturalistic data. Participants experienced two different test scenarios with two different ADAS warning configurations: Visual and audio alerts in the inactive state and in the active state. After activating alerts, 62%, 35%, 69%, 81% and 73% of drivers received less warnings in terms of FCW, UFCW, LDW, HMW and SLI respectively; the average of FCW, UFCW, LDW, HMW and SLI respectively declined 22%, 13%, 28%, 45% and 15%, but median changed little except for LDW and HMW. Then, the further analysis results by the one‐way analysis of variance (ANOVA) method revealed that all warnings except UFCW had positive effect on commercial truck drivers' behaviour performance. The findings provide important references for commercial truck driver training and supervision in the age of advanced technology.
This study aims to utilize a modified you only look once (YOLO) network to address the detection and classification of spilled loads on freeways. YOLO architecture was augmented in two ways. Firstly, ...a kernel size of 1 × 1 for the conv layers was used. Secondly, the use of connections between the convolution layers was proposed. For training the network, a synthetic dataset was constructed where ImageNet was used to choose ten types of spilled load objects and KITTI dataset as the background. The objects were blended in the KITTI images' road region, where the road area is segmented through an already trained network previously available. The testing dataset was constructed with manually taken photographs. Experiment results showed that the training model can arrive at an accuracy rate of 74%. The trained model was also demonstrated on the test set generated by taking the background images with camera mounted on a station wagon and on a field test.
Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that ...greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.
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