Roadside sensors have been widely installed on highways to collect full-sample real-time vehicle trajectory data, which supports full time and space control of traffic flow, microscopic driving behavior analysis, etc. However, rapid evaluating data quality is a challenge for management departments. Data quality evaluation methods in previous studies are limited to complicated operation and single dimension, which cannot meet the quality evaluation requirements of real-time vehicle trajectory data in dynamic traffic flow. In order to rapidly evaluate the quality of vehicle trajectory data from roadside millimeter-wave radar, a data quality evaluation method is proposed through mining the information of data. First, based on the typical errors of the measured trajectory data, 9 secondary evaluation metrics are established from four perspectives, including trajectory completeness, consistency, accuracy, and validity. Then, the comprehensive metric is calculated based on the CRITIC weighting method. Finally, an empirical analysis is conducted based on the vehicle trajectory data (3 549 in total) obtained by millimeter-wave radars in four different scenarios. The results show that the installation type and model of the millimeter-wave radar obviously influence the quality of vehicle trajectory data., and that the proposed evaluation method can distinguish the quality differences of vehicle trajectory data effectively. This study provides a support for the short-term performance decay monitoring and the type selecting of roadside sensors. Also, it gives a reference for improving the quality of vehicle trajectory data.

As the existing layout schemes of static wireless charging (SWC) facilities often neglect battery degradation costs, this paper proposes a layout optimization method of SWC facilities for electric buses by considering battery degradation characteristics. Firstly, by considering the operation characteristics of electric buses under opportunity charging mode, a layout optimization method of SWC facilities is developed with simultaneous consideration of charger deployment costs and battery degradation costs, with the function mechanism of battery state of charge (SOC) variety ranges on battery degradation rate being integrated into the model, and with the accumulated energy consumption constraints being introduced in the model to ensure that the SWC layout scheme can satisfy the bus route operation demands. Then, an improved TS (Tabu Search) algorithm is presented to solve the model by overcoming its computational complexity, and the initial solution and neighborhood structure of the algorithm are constructed according to the model characteristics. Finally, a numerical example is designed to verify the model and algorithm. The results indicate that the layout of SWC facilities has significant effects on battery degradation; that the proposed model can reduce 3.8% of the total annualized cost, as compared with the conventional model that neglects the battery degradation characteristics; that the battery degradation cost accounts for up to 72.3% of the total annualized cost under current battery technology and cost conditions; and that the improved TS algorithm is better than the original one because it significantly improves the solution efficiency. Moreover, a sensitive analysis is conducted to explore the impacts of multiple critical factors on optimal results, finding that both the upper bound of battery SOC and the SWC facility charging power have significant negative correlation with the total annualized cost, while the battery’s unit capacity cost, the SWC facility layout cost and the vehicle energy consumption rate all have positive correlation with the total annualized cost in various degrees.

In order to accurately recognize and estimate the lane-changing intentions of vehicles, a vehicle lane change intention recognition model based on TCN-LSTM network is proposed, which combines the temporal processing capability of TCN (Temporal Convolutional Network) with the gate memory mechanism of LSTM (Long Short Term Memory Network). In the investigation, firstly, the driving intentions of the target vehicle are divided into three types, namely going straight, changing lanes to the left, and changing lanes to the right. The running state indicators of the target vehicle and its surrounding neighboring vehicles (including the adjacent front and rear vehicles in the same lane, left lane and right lane) are extracted from the Citysim vehicle trajectory dataset using the median filtering algorithm. Secondly, to overcome the low recognition accuracy, long training time and slow parameter updating existing in statistical theories and traditional machine learning methods, the dilated convolution technique is used to extract the temporal features of time series, and the gate memory units are used to capture the long-term dependency relationships of temporal features. With 54 indicators, including the speed, acceleration, heading angle, heading angle change rate, and relative position information of the target vehicle and surrounding neighboring vehicles, as input parameters, and with the lane change intention of the vehicle as the output indicator, a vehicle lane -change intention recognition model based on the TCN-LSTM network is constructed. Finally, the recognition accuracy of TCN, SVM (Support Vector Machines), LSTM, and TCN-LSTM models under different input time steps are comparatively analyzed. The results show that, when the input time series length is 150 frames, the recognition accuracy of the TCN-LSTM model reaches a maximum of 96.67%; and that, in terms of overall classification accuracy, as compared with LSTM, TCN and SVM models, the TCN-LSTM model improves the classification correctness of lane change intention by 1.34, 0.84 and 2.46 percentage points, respectively, which demonstrates better classification performance.

In order to further clarify the differences in routing behaviors of different taxis, this paper adopted the method of frequent sequence mining to extract the frequent path between the same OD pairs, construct path sets, and analyze the similar characteristics of path sets from static and dynamic perspectives. By taking the trajectory data of taxis in Xi’an City as the research object, the path set between OD pairs is obtained through grid division and road network matching. Then, the frequent path is redefined, the PrefixSpan evolution algorithm is adopted, and the dynamic threshold and frequency index based on the obtained frequent subsequences are introduced to mine frequent paths. Furthermore, in order to complete the construction of three kinds of effective path sets, the shortest path and other paths are extracted, and the general properties of the constructed path sets are analyzed. Finally, the similarity between two-dimension time series (tracks) on the path is represented as dynamic similarity, and the similarity between one-dimension directed sequences (sections) is represented as static similarity, and the similarity analysis of three types of paths is carried out based on the improved longest common subsequence and dynamic time regularity algorithm. The results show that: (1) the similarity between the frequent path and the shortest path is rather high, meaning that most taxis still choose the road with the lowest travel time but not the shortest path; (2) time and distance are still the main considerations for travelers when choosing a path, but travelers do not completely pursue the shortest time or distance; (3) the calculated dynamic similarity is significantly higher than the static similarity, which means that the two-dimension sequential similarity on the path is higher than the one-dimension shape similarity; and (4) the two proposed methods both possess the highest similarity between the frequent path and the shortest path and the lowest similarity between the shortest path and other paths The consistency of the comparison results indicates that the similarity of the static path can be roughly measured by the that of the dynamic trajectory. The proposed frequent path mining algorithm is of certain reliability. It can provide supports for urban traffic managers with recommend routes and planed roads.

At present, the market share of ridesharing in China is relatively low, and there is still huge potential to be fully tapped in alleviating traffic congestion and reducing energy consumption and emissions. Incentive-based traffic demand management strategies can promote people’s willingness of ridesharing, but the design of incentive schemes is highly correlated with the market penetration of ridesharing. An unreasonable incentive scheme may easily lead to increased cost or even project failure. In order to further stimulate the potential ridesharing demand and make reasonable use of transportation resources, a road segment-based incentive optimization model for ridesharing is proposed, which uses ridesharing as the fulcrum and rewards as the lever to reduce the total social travel cost. The upper model of the proposed model aims to find the optimal incentive scheme to minimize the total social travel cost, and the lower model is a user equilibrium allocation model of ridesharing vehicles and single-driver vehicles under the incentive scheme. The iterative algorithm combining the genetic algorithm and the Frank-Wolfe algorithm is used to solve the upper and lower models, respectively, and the feasibility and effectiveness of the model are verified by using the Sioux Falls and Nguyen Dupuis transportation networks as numerical examples. The results show that budget investments that do not meet market penetrations may result in a significant increase in total social travel costs; and that, under the optimal incentive scheme, the total social travel cost is reduced by about 24.53%, the congestion of 50% of congested links is alleviated, and the fairness issue in traffic demand management is alleviated to a certain extent. Thus, there comes to the conclusion that the proposed model can provide theoretical support for managers to set up scientific and reasonable incentive-based ridesharing schemes.

With the development of autonomous driving technology, visual simultaneous localization and mapping (SLAM) technology has attracted more and more attention. In the memory parking scene, it is necessary to establish a prior map of the parking lot scene. Thus, when the car enters the same parking lot again, visual SLAM can help to construct and locate the scene. In order to improve the robustness, accuracy and efficiency of the map built by SLAM, first, a lightweight deep learning algorithm is used to improve the poor robustness of the traditional feature extraction algorithms in different scenarios, and the deep separable convolution is adopted to replace the previous common convolution structure, which greatly improves the time efficiency of feature extraction. Next, the Patch-NetVLAD algorithm is improved based on ResNet network, and the improved residual network as well as the original VGG network is retrained on MSLS data set. Then, image retrieval is used for rough positioning, candidate image frames are selected, and camera pose is solved by fine positioning to complete global initialization relocation. On this basis, the improved bag of words algorithm is used to retrain the images in different parking lot scenes, and all the algorithms are transplanted into the OpenVSLAM architecture to complete the mapping and positioning of the actual scene. The experimental results show that the proposed visual SLAM system can complete the construction of many scenes such as aboveground, underground and semienclosed parking lots, with an average longitudinal positioning error of 8.42 cm and an average horizontal positioning error of 8.30 cm, both of which meet the engineering requirements.

Selective catalytic reduction technology (SCR) is one of the commonly used technologies to reduce nitrogen oxide (NO _x ) emissions from heavy-duty diesel vehicles. The efficiency of NO _x conversion in the SCR system is closely related to the exhaust temperature. However, the existing NO _x emission models primarily focus on vehicle driving conditions, neglecting the correlation with exhaust temperature. Thus, it increases the uncertainty of NO _x emission measurement results, and challenges the establishment of emission inventory and the assessment of emission reduction policies. This study established a NO _x emission rate library and a model based on actual vehicle operating conditions and measured emission data. Subsequently, an exhaust temperature model utilizing vehicle specific power (VSP) and heat loss coefficient was developed. Based on this, based on the chemical reaction principle in the SCR system, a NO _x emission model incorporating exhaust temperature was derived. Finally, the proposed NO _x model and the MOVES model (MOtor Vehicle Emission Simulator) were employed to estimate NO _x emissions, which are then compared and analyzed against actual emissions. Results demonstrate the effectiveness of the proposed NO _x emission model in real-world conditions, with relative errors of 9.1%, 3.9%, and 3.3% observed across three heavy-duty diesel buses. These errors represent a reduction of 24.0, 13.1, and 16.3 percentage points, respectively, when compared to the MOVES model. Additionally, analysis of NO _x emission characteristics under different operating conditions reveals that the average NO _x conversion rate of heavy diesel trucks is 39.2 percentage points higher than that of diesel buses.

In order to improve the management level of autonomous vehicles and pedestrians at intersections, and thus improve the operational efficiency and stability of traffic flow, the paper constructed a management method for autonomous vehicles and pedestrians at intersections based on maximum pressure control and autonomous vehicle trajectory planning methods. Firstly, the queue length of pedestrians was modeled by the probability distribution function and comprehensively considering the influence of arrival rate, crosswalk length and width, waiting time and arrival distribution on pedestrians. Based on the estimated pedestrian queue length, the maximum pressure control was adopted to develop a queuing length management method for autonomous vehicles and pedestrians at intersections. Then, in order to help the internal autonomous vehicles at the intersection avoid collisions and obtain the best movement trajectory, the maximum pressure control method and the trajectory planning method of the existing intersection were planned on the basis of controlling the queue length of the self-driving vehicles and pedestrians at the intersection. Finally, Python and SUMO, which is an open source traffic simulation software, were used to verify the model. The simulation lasts for 2 hours. The simulation results show that the proposed autonomous car and pedestrian management method can not only control the trajectory of autonomous vehicles, but also quickly stabilize and reduce their delay and queuing lengths, and improve the efficiency of intersection operation.

As a self-driven travel mode, active travel or active mobility (AT or AM) plays a crucial role in alleviating urban traffic pressure and improving the temporal and spatial irrationality of urban traffic because of its small road occupation area, high mobility and good sustainability. The paper firstly studied the definition and connotation of active mobility. Secondly, starting from the spatiotemporal conditions affecting active mobility, it splited time and space into four types of constraints, namely, departure time, travel time, travel distance and built environment from the perspective of point and line, and expounded the characteristics of different types of spatiotemporal conditions in turn. It also analyzed the potential connection between active mobility and spatiotemporal constraints, and analyzed constraints influence on active mobility with single constraints and multiple spatiotemporal combinations as the entry point. Through summarization, it is found that the influence of spatiotemporal constraints on active mobility is more complicated than that in traditional cognition, and it is mainly a non-linear relationship and some of indexes have corresponding theoretical threshold. Finally, it put forward the shortcomings of the current theoretical research and the future research direction, which will provide a reference for the subsequent research. The purpose of this paper is to summarize the role of spatiotemporal constraints on active mobility, to show the specific change rule of movement travel under the role of different spatiotemporal constraints, aiming at changing the intrinsic thinking of travelers, improving the sharing rate of movement travel, and optimizing the travel structure of urban transportation.

The algorithmic personalized pricing of online taxi-hailing platforms has produced complex market impacts, and compared with traditional cab service, the order cancellation rate of online taxi passengers reaches about 30%. Therefore, it is worth exploring the impact mechanism of algorithmic personalized pricing on passenger cancellation rates and the key characteristics of whether passengers fulfill their orders. This paper tried to establish the causal mechanism between algorithmic personalized pricing and passengers’ order cancellation rate using rectangular Hotelling model. Using a Stackelberg game model between two taxi-hailing platforms, it revealed the relationship among discriminatory pricing, passenger cancellation rate, and competition intensity between two platforms. Furthermore, based on the big data of online taxi-hailing platform orders, this paper applied some inductive learning tools such as Bhattacharyya distance, Gradient Boosting Decision Tree (GBDT) and improved Las Vegas method for wrapper-method feature selection to data mining of millions of orders on online taxi-hailing platforms to find out the key features that determine whether passengers take the orders or not. Analysis results show that the final consumption choice of passengers mainly depends on the price factors. And improving the match and dispatch strategies to reduce passengers’ waiting time can significantly improve fulfillment rate. The results are helpful for taxi-hailing platform to appropriately design the pricing and operation strategies to maintain the number of customers in the two-sided markets, which ensures the sustainable and successful operation of the platform. Meanwhile, it will provide a theoretical basis for antitrust authorities to intervene in platform personalized pricing.

In view of the problem that passengers motivate ride-hailing drivers in the form of red packet or dispatching fees to realize self-scheduling, this paper studied the interactive relationship between passenger-ride-hailing matching decision and incentive strategy choice. Based on the matching equilibrium in taxi-sharing, this paper designed a matching equilibrium model for taxi-sharing with peer-passenger incentive mechanism, taking the maximization of total passenger surplus as the goal and considering the constraints such as matching, equilibrium and cost. From the perspective of passengers, it designed the passenger incentive strategy, ride-hailing incentive strategy, passenger and ride-hailing incentive strategy, and the three incentive strategies were embedded into the column generation algorithm to solve the model and to achieve matching equilibrium and pricing equilibrium. By empirical analysis of Dalian taxi data, the results show that, compared with with only motivating drivers from the supply side in the form of random dispatching fees, the implementation of incentive strategies from the demand side and the supply side can promote taxi-share, and the passenger surplus can be increased by 12.6%. When the demand is larger than the supply, about 26% of incentive is transferred among peer passengers for more taxi-share matches. The fare discount rate also affects the flow of incentives. Using incentive strategies and discount strategies simultaneously can avoid malicious competition and ineffective incentives. By increasing the number of taxi-sharing trips, we can simultaneously reduce travel costs for passengers and boost driver incomes.