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With the increasing density of the freeway network, frequent traffic incidents on road segments have a significant impact on the operational efficiency of the road network. Therefore, it has become urgent and important to study traffic route guidance strategy on the road network level. The previous traffic route guidance method primarily focused on the congestion on the road segments where incidents occurred, with insufficient attention given to the impact of congestion on the road network level. In this study, a route guidance model with limited overlap is proposed to improve freeway network reliability under traffic incidents. Specifically, in order to explore alternative paths, we conducted a study on the problem of finding k-short paths with limited overlap. The objective is to identify a set of k-paths that are both sufficiently dissimilar and as short as possible. Then, we promptly update the route guidance information using a stochastic dynamic traffic assignment model that aligns with travelers' path choice psychology. Moreover, we use the reliability of the road network to evaluate the network performance. To illustrate the model, the Jinan freeway network is selected as an experimental study. The effectiveness of this method was validated through SUMO simulations, comparing it with alternative route guidance methods, including Yen's algorithm, [sup.A∗] algorithm, and ant colony algorithm. These results show that the proposed method has proven effective in mitigating traffic congestion arising from incidents and performs well in regard to the reliability of the road network under the impact of incidents., Author(s): Xuan Zhang [1]; Jinjun Tang (corresponding author) [1]; Chengcheng Wang [2]; Chao Wang [2] 1. Introduction Freeway network plays an important role in establishing connectivity among cities. They are [...]

The emergence of connected autonomous vehicles (CAVs) has the potential to dramatically increase road capacity, and the provision of dedicated lanes for CAVs is a common management strategy to ensure that CAVs take appropriate priority over other vehicles. However, existing models of CAV lane management do not perform well at low CAV market penetration rates. Therefore, a novel model is proposed to address this shortcoming in this paper, and qualified human‐driven vehicles (HDVs) are allowed to enter the CAV priority lane when there is a low CAV penetration rate. A theoretical model is proposed to examine the impacts of three managed lane strategies under mixed traffic and then compare three lane management strategies under different CAV penetration rates and traffic demands. The results show that these lane management strategies can improve throughput, reduce travel time, increase speed, and reduce the variance of the distribution of vehicle speeds under different penetration rates and traffic demands. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of time and distance gaps between the primary (PC) and secondary crashes (SC) is crucial for preventing SC ccurrences and improving road safety. Although previous studies have tried to analyse the variation of gaps, there is limited evidence in quantifying the relationships between different gaps and various influential factors. This study proposed a two-layer stacking framework to discuss the time and distance gaps. Specifically, the framework took random forests (RF), gradient boosting decision tree (GBDT) and eXtreme gradient boosting as the base classifiers in the first layer and applied logistic regression (LR) as a combiner in the second layer. On this basis, the local interpretable model-agnostic explanations (LIME) technology was used to interpret the output of the stacking model from both local and global perspectives. Through SC dentification and feature selection, 346 SCs and 22 crash-related factors were collected from California interstate freeways. The results showed that the stacking model outperformed base models evaluated by accuracy, precision, and recall indicators. The explanations based on LIME suggest that collision type, distance, speed and volume are the critical features that affect the time and distance gaps. Higher volume can prolong queue length and increase the distance gap from the SCs to PCs. And collision types, peak periods, workday, truck involved and tow away likely induce a long-distance gap. Conversely, there is a shorter distance gap when secondary roads run in the same direction and are close to the primary roads. Lower speed is a significant factor resulting in a long-time gap, while the higher speed is correlated with a short-time gap. These results are expected to provide insights into how contributory features affect the time and distance gaps and help decision-makers develop accurate decisions to prevent SCs. [ABSTRACT FROM AUTHOR]

Urban public transportation is crucial for achieving low-carbon cities but faces challenges due to urban planning and transportation coordination issues. This study introduces a joint decision model for multiple public transportation options, grounded in the cross-nested logit (CNL) framework, integrating departure time and travel mode dimensions. It combines three key variables—travel scheme, built environment, and carbon emissions—to assess transportation choices comprehensively. CO 2 emissions are evaluated with the COPERT (COmputer Programme to calculate Emissions from Road Transport) model for accurate environmental impact assessments. Utilizing multi-source data from Shenzhen, including smart card, vehicle trajectory, and order data, the study evaluates factors influencing public transportation choices and compares emission reduction potentials. Results indicate that Shenzhen travelers are more sensitive to traffic and built environment service levels when choosing departure times rather than travel modes. The findings highlight the significant role of metros and shared bicycles in reducing emissions and suggest built environment adjustments to promote these low-carbon travel modes. Additionally, elasticity analysis and Monte Carlo simulations provide insights into future strategies under different emission reduction scenarios. These findings offer a theoretical basis and practical guidance for improving transportation structure and built environment, supporting the development of sustainable and low-carbon cities. • A multi-dimensional choice model considering built-environment factors is proposed. • The emission reduction capacity of various public transportation choices is explored. • An elastic analysis is conducted between travel emissions and multiple trip options. • The proportions of alternatives are simulated in varied emission reduction scenarios. [ABSTRACT FROM AUTHOR]

The incoordination between public transportation system construction and urban infrastructure development is a challenge for the sustainable development of cities. Exploring the associations between the built environment, transportation, and carbon emissions from a macro perspective is significant for realizing the goal of low-carbon urban transportation. This study proposes a collaborative optimization framework for the built environment and public transportation structure under carbon emission reduction. The purpose is to enhance the services capacity and emission reduction potential of public transportation with limited resources. Six factors are considered to construct the many-objective optimization model, including carbon emissions, energy consumption, government subsidies, time and economic costs, and road network resource occupation. The parameters of the model are calculated based on various multi-mode travel data (including vehicle order data, vehicle global positioning system (GPS) trajectory data, and intelligent card (IC) data) and built environment data. During the process, a data processing method for identifying bus drop-off points and inferring urban functional areas is developed. Then, the NSGA–III–DE (Differential Evolution) algorithm is designed to obtain the optimal solutions. The methodological framework is validated in the experiment implemented in Shenzhen city. According to the hypervolume (HV) value, the performance of NSGA–III–DE is compared with that of NSGA-II and NSGA-III. The results show that NSGA–III–DE has better global search ability and presents stable performance for different mutation operators. Finally, the optimization results are further discussed to provide effective guidance for urban planning and low-carbon transportation. • The associations of urban built environment, transportation and carbon emissions are explored. • A multi-mode public transportation optimization model under low-carbon target is proposed. • NSGA–III–DE performs a better global search ability in finding excellent solutions. • The data processing methods are provided for inferring bus drop-off points and functional area. [ABSTRACT FROM AUTHOR]