Mobile Phone Signaling (MPS) data record the daily traces of urban residents, offering a cost-effective means to obtain travel information for urban traffic management and planning at low cost. However, despite the vast amount of data available, there remains a lag in the development of techniques for identifying fine-grained information such as travel modes. On the one hand, the high positioning error and irregular collection frequency make the identification of fine-grained modes challenging. On the other hand, the difficulties in collecting real labeled data limit the training and evaluation of advanced models. In this paper, we present an advanced Travel Mode Identification (TMI) framework and collect real labeled MPS data for evaluation. Specifically, a fast smoothing method is proposed to enhance noise reduction in large-scale trajectories while effectively mitigating positioning errors. We propose novel point-level bus route alignment features for advanced deep-learning models to improve differentiation between motorized modes. Furthermore, a deep learning model with ensembled feature encoding modules is designed to overcome training instability due to limited data amount. Our proposed framework achieves an accuracy of 83.06% in identifying fine-grained travel modes, including walking, riding, bus, car, and metro, with recall rates exceeding 78% for all modes except walking. We analyze the relationship between accuracy and the spatiotemporal characteristics of trajectories, revealing a significant impact from the collection frequency, while showing insensitivity to distance gaps and positioning errors. This study demonstrates the potential of MPS for TMI and promotes its application in intelligent transportation systems.

Vehicle re-identification aims to match and identify the same vehicle crossing multiple surveillance cameras and obtain traffic information such as travel time. The Automatic License Plate Recognition (ALPR) data are widely employed in urban surveillance. However, vehicle re-identification based on ALPR data is challenging due to license plate recognition errors and unrecognized issues. This paper proposes a vehicle matching algorithm designed to maximize the travel time probability using ALPR data, while accounting for recognition errors and unrecognized issues. The proposed algorithm consists of several modules, including the estimation of travel time distribution, computation of travel time probability, calculation of travel time confidence intervals and matching time window size, restricted fuzzy matching, and vehicle matching optimization. To evaluate the effectiveness of the proposed algorithm across varying lighting and weather conditions, ALPR data was collected from a survey road in four scenarios: sunny day, sunny night, rainy day, and rainy night. The results indicate that when compared to a sunny day scenario, severe lighting and adverse weather conditions lead to decreased matching accuracy and increased matching accuracy errors for all methods evaluated. However, our proposed model outperforms benchmark algorithms in both scenarios, demonstrating its superior performance.

Understanding human mobility has become a greater demand in recent years. Among them, how to extract people’s travel trajectories and reconstruct them accurately is crucial to explain people’s mobility. Due to its spatio-temporal uncertainty and sparsity, however, map matching based on mobile phone signaling data (MSD) remains a challenge. In this paper, we introduce an innovative and precise Map Matching Method tailored for MSD, leveraging an incremental Hidden Markov Model (HMM) algorithm. A series of effective modules are put forward to address several critical challenges associated with MSD, including handling drifting data, ping-pong sequences, spatio-temporal sparsity, location uncertainty, back-and-forth movements, and U-turn errors. Then Incremental HMM is applied to provide the optimal path on the digital map. To assess the effectiveness of our proposed model, we conducted a comprehensive comparison against state-of-the-art methods and conducted ablation experiments. The results highlight that our model outperforms existing approaches, achieving 82.9%, 93.6%, and 0.88 in precision, recall and F1 based on the distance measurement, respectively. The proposed method makes it possible to work on real-time map matching based on large-scale MSD. In the future, we will conduct the exploration of human mobility based on MSD in cities and congestion detection of network traffic states.

The collection of individual GPS data, as a substitute for traditional travel surveys, is hindered by low response rates and high costs. Meanwhile, passive mobile phone data, such as Location-Based Service (LBS) data, has high user penetration but remains unexplored for travel behavior analysis. The irregular frequency of data collection results in distorted features and non-uniformly distributed information of trajectories in the Travel Mode Identification (TMI) task. The diversity of trajectories also poses challenges for TMI models. To fill the gap, we propose a TMI framework named Trajectory-as-a-Sequence for Non-uniform data (TaaSN). Specifically, we incorporate GIS features to address the sparsity of motion features. Then, we design a model structure that accounts for time gaps, capturing non-uniform information of trajectory due to irregular frequency. To further enhance model’s generalizability to diverse trajectory data, we propose a trajectory point dropout training strategy. The experimental results demonstrate that the TaaSN framework can greatly exploit the potential of LBS data in travel behavior mining. The proposed model achieves high accuracy on both non-uniform and uniform trajectories. On pseudo-LBS data, the accuracy reaches 84.9% when applied to the trajectories of existing travelers, and achieves 83.5% when applied to the trajectories of new travelers. On uniform data, it reaches 86.8% and 85.4%, respectively. Furthermore, we carry out comprehensive experiments and conclude valuable insights for future TMI framework design.

A freeway stretch with even one pair of unmeasured on/off-ramps is not fully observable in traffic states. Flow observability is essential for freeway traffic modeling, surveillance, and control. It is a longstanding and tricky issue to estimate flows for unmeasured ramp pairs. This problem seems to be hardly tractable in conventional approaches, and this paper intends to handle it based on machine learning. The work was partially inspired by transfer learning. Consider that no measurements are available for a target ramp pair, and the knowledge about ramp flow estimation may be drawn from other (measured) ramp pairs, provided that measured and unmeasured ramp pairs would share similarities in some key traffic flow patterns. Two simple machine learning algorithms, random forest (RF) and gradient boosting machine (GBM), were employed to this end. RF and GBM were driven by real measurement data to establish models that relate ramp flows to adjacent mainstream traffic conditions. The models were then applied for our task. The estimation performance was evaluated using real measurement data from the Shanghai Urban Expressway and the Intercity Highway in California, with satisfactory results obtained.

This paper makes a basic assumption that energy conservation exists, between psychological potential and a vehicle's kinetic energy, in the driver's psychological field based on the driver's mental activities. A virtual spring is used to describe the storage and release of psychological potential energy. Under the aforementioned conditions, we established a macroscopic traffic flow model with conservation law. Each parameter in the new model is physically meaningful and explicit. Additionally, the model can fit field data consistently well, both in free-flow and congested situations. The results of this paper prove the rationality of the energy conservation concept in traffic flow, which improves the understanding of traffic flow and provides a new theoretical foundation.

This paper investigates variable speed limit (VSL) systems, trying to optimize the system designs when the variable message signs (VMSs) are movable. The optimization problem is formulated as a large mixed-integer nonlinear programming problem, whose decision variables include the number of VMSs to be deployed, the locations of the VMSs, and the speed limits posted on the VMSs. Two objectives are considered, one is to smooth the flow propagation, and the other is to minimize the environmental impact of freeway traffic. Moreover, a genetic algorithm is proposed to solve the complex problem. Numerical examples performed on a real freeway segment show that VSL can effectively achieve smooth flow and reduce the environmental impact of freeway traffic.