Vehicle trajectory data are critical to urban active traffic management and simulation applications. Automatic license plate recognition (ALPR) data can provide partial vehicle trajectory information by matching the detected vehicle license plates through time series. However, the trajectory extracted from ALPR data tend to be sparse and incomplete due to technical and financial constraints. This paper deals with the problem of sparse trajectory reconstruction based on ALPR data. Firstly, the multiple travel activities of the vehicle are divided based on the reasonable travel time threshold, and the incomplete vehicle trajectory is identified. Then, candidate trajectories are generated by an improved K-shortest-path (KSP) algorithm based on space-time prism theory. Finally, the auto-encoder model is utilized to select the candidate trajectory with optimal decision indicators, which realizes the vehicle trajectory reconstruction. The proposed method was implemented on a realistic urban traffic network in Ningbo, China. The verification results show that the proposed method has a comprehensive accuracy of 85% and good robustness. From the comparison with the baseline algorithm, it can be seen that the proposed method still has high accuracy in low ALPR coverage rate, and there exists a minimum required ALPR coverage rate (50% in the test network) for reconstructing trajectories accurately.INDEX TERMS Automatic license plate recognition, space-time prism, auto-encoder, trajectory reconstruction.

It is different from the previous supervised learning algorithm based on personal travel questionnaire, the aim of this study is to develop an unsupervised learning methodology to estimate the docked bike-sharing users' trip purposes using IC card data, which trip purposes were unknown from the dataset. The present study is able to extract the trip-chains, which is used to understand the complete individual trip process. A rigorous method is then proposed to interpret the purpose of each leg of the tripchain using a continuous hidden Markov model (CHMM). This method effectively combines the Gaussian mixture model and the hidden Markov model, and realizes the inference based on trip-chains. It is intended to enhance the understanding of docked bike-sharing users' transfer intention, which is different from most trip motivation recognition methods. The Gaussian mixture layer uses the feature space constructed by the spatial and temporal information on trip-chains from the IC card data, as well as the land-use characteristics of the docked bike-sharing docking stations to complete the transfer of the trip-chains to the trip modes. The hidden Markov structure can realize the process from the trip modes to the trip purposes. The IC card data of docked bike-sharing usage in Nanjing, China is used to interpret the specific steps of the proposed model. A questionnaire survey is conducted to obtain the real trip purposes, which is compared with the estimated results from the model to verify the effectiveness of the model.. The results show that the accuracies of single trip recognition and chain trip recognition are 0.770 and 0.756, respectively. Compared with the baseline algorithm, the model also shows good performance. Therefore, the proposed approach can be used to discover and interpret the trip purpose using the IC card data. INDEX TERMS Continuous hidden Markov model, IC card, docked bike-sharing, trip-chain, trip purpose

The active traffic management system disseminates traffic information to drivers to guide their route choice, so as to alleviate traffic congestion. Most drivers are highly dependent on mobile navigation APP, and the display form of mobile navigation information is of great importance. In order to improve drivers’ compliance with guidance information, this research accommodates for information that disseminate time attribute (when to provide the traffic information) and display format attribute (voice navigation or not, flat map or three‐dimensional [3D] map) and also explores the interaction effects between individual and trip characteristics. A revealed preference (RP) and stated preference (SP) survey consisting of 831 respondents is conducted in Nanjing, China. Then three kinds of panel logit model are established to fit the survey data. According to the model results, it is found that drivers with aggressive style are more sensitive to travel time, and drivers who are familiar with the road network are more sensitive to the 3D map with voice navigation format. The results of parameter estimation are also used for the trade‐off analysis of various exogenous variables. The findings of this study provide useful insights for the development of mobile navigation APPs, personalized path recommendation and congestion pricing strategies.