Transportation Research Part C: Emerging Technologies

Journal: Transportation Research Part C: Emerging Technologies

Abbreviation

Transp. Res., Part C Emerg. Technol.

Publisher

Elsevier

ISSN

0968-090X

Show all metadata

Search Results

Publications 1 - 10 of 59

MoGERNN
Zhou, Qishen; Zhang, Yifan; Makridis, Michail; et al. (2025)
Transportation Research Part C: Emerging Technologies
Given a partially observed road network, how can we predict the traffic state of interested unobserved locations? Traffic prediction is crucial for advanced traffic management systems, with deep learning approaches showing exceptional performance. However, most existing approaches assume sensors are deployed at all locations of interest, which is impractical due to financial constraints. Furthermore, these methods are typically fragile to structural changes in sensing networks, which require costly retraining even for minor changes in sensor configuration. To address these challenges, we propose MoGERNN, an inductive spatio-temporal graph model with two key components: (i) a Mixture of Graph Experts (MoGE) with sparse gating mechanisms that dynamically route nodes to specialized graph aggregators, capturing heterogeneous spatial dependencies efficiently; (ii) a graph encoder-decoder architecture that leverages these embeddings to capture both spatial and temporal dependencies for comprehensive traffic state prediction. Experiments on two real-world datasets show MoGERNN consistently outperforms baseline methods for both observed and unobserved locations. MoGERNN can accurately predict congestion evolution even in areas without sensors, offering valuable information for traffic management. Moreover, MoGERNN is adaptable to the changes of sensor network, maintaining competitive performance even compared to its retrained counterpart. Tests performed with different numbers of available sensors confirm its consistent superiority, and ablation studies validate the effectiveness of its key modules. The code of this work is publicly available at: https://github.com/ZJU-TSELab/MoGERNN
Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data
Hörl, Sebastian; Balac, Milos (2021)
Transportation Research Part C: Emerging Technologies
Synthetic populations of travelers and their detailed mobility behavior are an important basis for agent-based transport simulations, which are increasingly used in transport planning and research today. To date, research based on such simulations is rarely replicable as it is based on proprietary data and tools. To foster the discussion and steer research towards reproducible transport simulations, this paper introduces a process for generating a synthetic travel demand with individual households, persons, and their daily activity chains for Paris and its surrounding region Île-de-France — entirely based on open data and open software and replicable by any researcher. The resulting travel demand is published for others to use as a comprehensive data basis for agent-based transport simulations and as a test bed for population and demand synthesis algorithms. Furthermore, it is discussed how implicit correlation structures impact the potential use cases of the synthetic travel demand for simulation and analysis purposes and how the common practice of using population samples for downstream simulations affects the results.
Developing a passive GPS tracking system to study long-term travel behavior
Marra, Alessio Daniele; Becker, Henrik; Axhausen, Kay W.; et al. (2019)
Transportation Research Part C: Emerging Technologies
Sparse Gaussian process-based strategies for two-layer model predictive control in autonomous vehicle drifting
Hu, Cheng; Xie, Yangyang; Xie, Lei; et al. (2025)
Transportation Research Part C: Emerging Technologies
Vehicle safety is paramount in autonomous driving, particularly when managing vehicles at extreme side-slip angles-a challenge often overlooked by conventional controllers. Recent studies have focused on vehicle drift control under such extreme conditions. However, tracking complex trajectories while drifting is challenging, especially in the presence of a model mismatch. This paper proposes a two-layer model predictive controller based on sparse variational Gaussian processes. The first layer is responsible for computing the optimal drift equilibrium points, while the second layer is tasked with tracking these points. A variational free energy-based Gaussian process is utilized to compensate for errors in the upper-layer drift equilibrium point calculations and mismatches in the lower-layer controller model. Moreover, the vehicle's state is determined to be either in transit drift or deep drift based on whether the slip angle and steering angle have reached critical values. Gaussian models are established for each state to enhance prediction accuracy. The effectiveness of the controller is demonstrated through joint simulations on MATLAB and CarSim platforms. First, the proposed two-layer model predictive controller was compared with three state-of-the-art drift controllers, demonstrating at least a 48.64% reduction in average lateral error when tracking trajectories with varying curvature. Second, when combined with sparse Gaussian processes, the controller's learning ability was validated in scenarios with a 5% to 20% friction coefficient mismatch. Specifically, in the scenario with a 20% friction coefficient mismatch, its average lateral error was reduced by 95.09% after model error learning. Additionally, the controller was compared with both Fully Independent Training Conditional (FITC) GP-based MPC and Full GP-based MPC controllers, demonstrating better trajectory tracking capability and model error learning ability.
Conserved quantities in human mobility
Hong, Ye; Martin, Henry; Xin, Yanan; et al. (2023)
Transportation Research Part C: Emerging Technologies
Quantifying intra-person variability in travel choices is essential for the comprehension of activity-travel behaviour. Due to a lack of empirical studies, there is limited understanding of how an individual’s travel pattern evolves over months and years. We use two high-resolution user-labelled datasets consisting of billions of GPS track points from ∼3800 individuals to analyze individuals’ activity-travel behaviour over the long term. The general movement patterns of the considered population are characterised using mobility indicators. Despite the differences in the mobility patterns, we find that individuals from both datasets maintain a conserved quantity in the number of essential travel mode and activity location combinations over time, resulting from a balance between exploring new choice combinations and exploiting existing options. A typical individual maintains ∼15 mode-location combinations, of which ∼7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well modelled using a power-law fit that slows down over time. Our findings enrich the understanding of the long-term intra-person variability in activity-travel behaviour and open new possibilities for designing mobility simulation models.
Integrated optimization of capacitated train rescheduling and passenger reassignment under disruptions
Hong, Xin; Meng, Lingyun; D'Ariano, Andrea; et al. (2021)
Transportation Research Part C: Emerging Technologies
During railway operations, unexpected events may influence normal traffic flows. This paper focuses on a train rescheduling problem for handling large disruptions, such as a rolling stock breakdown leading to a cancelled train service, where passenger reassignment strategies have to be considered. A novel mixed-integer linear programming formulation is established with consideration of train retiming, reordering, rerouting, and reservicing (addition of extra stops). The proposed mathematical formulation considers planning extra stops for non-canceled trains in order to transport the disrupted passengers, which were supposed to travel on the canceled train, to their pre-planned destination stations. Other constraints deal with limited seat capacity and track capacity, and mapping train rescheduling with passenger reassignment. A bi-objective function is optimized by a weighted-sum method to maximize the number of disrupted passengers reaching their destination stations and to minimize the weighted total train delay for all non-canceled trains at their destinations. A series of numerical experiments based on a part of the Beijing-Shanghai high-speed railway line is carried out to verify the effectiveness and efficiency of the proposed model and to perform a sensitivity analysis of various performance factors. The results show that an optimal reassignment plan of disrupted passengers is important to achieve real-time efficiency of traffic and re-ticketing. The impact of passenger reassignment on train rescheduling is influenced by the weights for objectives, duration of disruption, allowed additional dwell and running times, and relationship between passenger demand and total available train capacity.
Adaptive control with moving actuators at motorway bottlenecks with connected and automated vehicles
Du, Yu; Makridis, Michail; Tampère, Chris M.J.; et al. (2023)
Transportation Research Part C: Emerging Technologies
Connected and automated vehicles (CAVs) have the potential to improve the operation of future road traffic systems. In this paper, we propose a control method that uses CAVs as dynamic actuators to improve the capacity at motorway bottlenecks robustly. The proposed approach has been designed for mixed traffic flow using the fundamental diagram model of mixed traffic flow as a control activation tool. In order to implement our approach, we assume that the availability of detectors at motorway are able to obtain the density in real-time. The idea is that assuming a certain percentage of CAVs presence on the road, such vehicles can be used as mobile actuators to perform speed coordination tasks. Furthermore, the aim is to transfer the delays observed at the bottlenecks upstream on the motorway, where the conditions are more homogeneous. The proposed approached can be generalized and used in bottleneck scenarios with or without additional inflow from an on-ramp. According to the designed control strategy, when the traffic density at the bottleneck satisfies the activation condition, the CAVs will shift to moving actuator mode and generate a new speed profile. The objective is to improve traffic flow at the downstream bottleneck and also smooth the upstream arrival vehicle speed, thus improving the overall throughput of the network. The method has been evaluated through microscopic simulation experiments conducted with scenarios on the real-case study, a motorway in Antwerp, Belgium. The results show significant improvements in reducing traffic density and improving travel speed both locally at the control area, as well as at network level. Comparative experiments under different penetration rates show that the proposed method remains robust to the percentage of CAVs on the road. Finally, it can significantly reduce vehicle delays and prevent over-congested conditions, and also improve the traffic flow rate, even for relatively low penetration rates of CAVs.
A parsimonious enhanced Newell’s model for accurate reproduction of driver and traffic dynamics
Zheng, Shi-Teng; Jiang, Rui; Jia, Bin; et al. (2023)
Transportation Research Part C: Emerging Technologies
This paper investigates the stimulus–response behavior between leader and follower in car following, based on vehicle trajectories in the prevailing field experiments. The analysis result indicates that the follower’s reaction time is time-varying, which can change significantly or keep a constant value for some time; and the follower cannot accurately track the leader in car following, which results in a residual from the follower’s speed. Inspired by the findings, this paper proposes a parsimonious enhanced Newell’s car-following model incorporating the stochastic reaction time and the fluctuation around the vehicle’s desired speed subject to the mean reversion process. The numerical experiment is carried out. It is shown that the proposed model can qualitatively and quantitatively reproduce the following important field observations: (i) the spontaneous formation and evolution of traffic oscillations, (ii) the concave growth pattern of traffic oscillations, (iii) the oscillations’ amplitude and frequency, (iv) the stochastic reproduction of individual trajectories, and (v) the linear speed-capacity relationship. The robustness of the proposed model is demonstrated, compared with the state-of-the-art model. Finally, the sensitivity analysis is carried out to evaluate the effect of each parameter of the proposed model.
Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland
Reck, Daniel Jan; Guidon, Sergio; Haitao, He; et al. (2021)
Transportation Research Part C: Emerging Technologies
Shared micromobility services (e-scooters, bikes, e-bikes) have rapidly gained popularity in the past few years, yet little is known about their usage. While most previous studies have analysed datasets from single providers, only few comparative studies of two modes exist and none so-far have analysed competition or mode choice at a high spatiotemporal resolution for more than two modes. To this end, we develop a generally applicable methodology to model and analyse shared micromobility competition and mode choice using widely accessible vehicle location data. We apply this methodology to estimate the first comprehensive mode choice models between four different micromobility modes using the largest and densest empirical shared micromobility dataset to-date (~169M vehicle locations collected in Zurich over two months). Our results suggest that mode choice is nested and dominated by distance and time of day. Docked modes are preferred for commuting. Hence, docking infrastructure for currently dockless modes could be vital for bolstering micromobility as an attractive alternative to private cars to tackle urban congestion during rush hours. Furthermore, our results reveal a fundamental relationship between fleet density and usage. A "plateau effect" is observed with decreasing marginal utility gains for increasing fleet densities. City authorities and service providers can leverage this quantitative relationship to develop evidence-based micromobility regulation and optimise their fleet deployment, respectively.
Evaluating resilience in urban transportation systems for sustainability: A systems-based Bayesian network model
Tang, Junqing; Heinimann, Hans; Han, Ke; et al. (2020)
Transportation Research Part C: Emerging Technologies
This paper proposes a hierarchical Bayesian network model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The model takes into account various factors collected from multi-source data platforms involved in stages of design, construction, operation, management, and innovation in road transportation systems. We test the model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience scores of all four cities’ transportation systems are within a moderate range with values between 49% to 59%. Although they all have an ever-increasing economic level, the levels of transportation resilience in Beijing and Tianjin decrease first and then gradually increase in a long run, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that decision makers should pay more attention to the capabilities of quickly rebuilding and making changes to cope with future disturbances. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building resilient infrastructure. © 2020 Elsevier

Publications 1 - 10 of 59

Journal: Transportation Research Part C: Emerging Technologies

Abbreviation

Publisher

Journal Volumes

ISSN

Description

Filters

Search Results