Journal of Advanced Transportation最新文献

Hazardous materials pose significant dangers during transportation due to their flammable and explosive properties. The consequences of accidents involving such materials are often severe and irreparable. A well-designed hazardous materials transportation network can mitigate these risks. However, designing such a network presents two major challenges: quantifying the risk associated with hazardous materials transportation and addressing the hierarchical relationship between government and companies. To address these challenges, we enhance the accuracy of accident probability estimates and the comprehensiveness of accident consequence assessments, incorporating the uncertainty of accident outcomes. We propose a comprehensive risk assessment model and develop a bilevel programming model to reflect the hierarchical relationship. In this model, the government at the upper level aims to minimize the total risk, while companies at the lower level seek to minimize their total costs. The model is transformed using chance-constrained programming and solved using heuristic algorithms. We apply the model to the highway network in Anhui province, China, to verify its validity. The results demonstrate that the model effectively manages the hierarchical relationship between government and companies, reduces the risk of hazardous materials transportation, and enhances the stability and safety of the transportation network.

The primary objective of this paper is to minimize the overall travel costs for passengers while simultaneously maximizing the operational revenue for the transportation company. This is achieved through the optimization and adjustment of various factors, such as the intervals between regular bus and subway services, the duration of vehicle stops at each station, and the pricing structure for subway and shared bicycle usage. By enhancing the efficiency of passenger travel, we have successfully bolstered the company’s operational profits. In contrast to prior research, this paper comprehensively considers the dual uncertainties associated with both bus operations and shared bicycle operations within a cooperative system. By establishing a coordinated dual-level optimization model for regular bus, subway, and bike-sharing networks under dual uncertainty conditions, we employed convex combination techniques to unify the dual uncertain variables into a single objective, which was then incorporated into a chance-constrained bilevel programming model. Ultimately, we utilized KKT conditions to transform the model from a bilevel to a single level for resolution. This paper centers its research on the collaborative system comprising the Nanchang Metro Line 1, Bus Route 520, Bus Route 211, and the adjacent region hosting a cluster of shared bicycles. By leveraging Python programming, optimization models, empirical data on traffic flow and stoppage times, and OD data, we conducted an optimization analysis to solve the problem at hand. According to the optimization results, passenger waiting time, passenger transfer time, and passenger on board time are effectively reduced by 6.81%, 18.29%, and 23.92%. At a confidence level of 95%, the resulting time level results in a 12.44% reduction in total travel time. The average subway fare increased by 18.12%, the average shared bicycle fare decreased by 19.12%, and the total cost of travel expenses increased by 16.68%. The final total cost of travel was reduced by 4.06%, and the business operating income was increased by 13.10%. The comprehensive optimization results have effectively fulfilled the objectives of the bilevel optimization model, thereby confirming the rationality and practicality of the optimization approach. The research outcomes hold significant practical implications for facilitating the efficient and cooperative development of urban transportation networks, ultimately enhancing the convenience of residents’ travel experiences.

Probe vehicles and loop detectors collect traffic data for empirical MFD estimation. The accuracy of MFD estimation is contingent upon the percentage of road network links covered (road coverage) and the extent of information (penetration rate) from each link. While the previous research has explored the effects of loop detector road coverage and probe vehicle penetration rates, the impact of probe vehicle road coverage remains unaddressed. Furthermore, the unequal distribution and low penetration rates of probe vehicles undermine the reliability of the data obtained from them. Therefore, this study aimed to (i) examine the impact of probe vehicle road coverage on MFD estimation, (ii) enhance the reliability of the spatial data by using speed data reconstruction, (iii) highlight the increased role of spatial data in the accuracy of MFD estimation, and (iv) develop a performance factor of speed data quality to demonstrate that the quality of spatial data is more susceptible to the penetration rate than road coverage. The results underlined the role of FCD road coverage in MFD estimation, and the proposed methodology significantly improves spatial data quality. The study concluded that spatial and temporal data affect the MFD estimation differently, and the proposed performance factor increases the role of spatial data in MFD estimation.

India initiated its ethanol fuel blending program (EBP) two decades ago to enhance energy security, reduce crude imports, and promote low-carbon transportation. However, despite government initiatives and policies, the EBP has made slower progress than anticipated. For the long-term adoption and success of the EBP, the following critical areas must be analyzed: integrated ethanol production from multiple feedstocks, demand and linkage to industries requiring ethanol, impact on the environment and revenue prospects, and evaluation of the policy measures adopted. This study addresses these topics by analyzing the interaction between various industries (demand) and ethanol production from multiple sources (supply) using system dynamics modeling. Simulation and scenario analysis have been used to evaluate the environmental and economic performance of ethanol blends under the influence of various policy parameters. The findings indicate that, contrary to the conventional belief, the production of ethanol directly from sugarcane juice does not significantly threaten food security. Higher blending ratios yield enhanced environmental benefits and revenues in the short term, but these are outweighed by the long-term benefits of lower blending ratios. The findings also indicate that encouraging second-generation ethanol production from rice stalks and increasing the blending ratios will reduce CO₂ emissions. However, the goals set for blending cannot be achieved until measures to diversify feedstocks and improve the infrastructure for ethanol production are scaled up.

Transit performance is greatly influenced by its accessibility, which considers the spatial distribution of transit facilities with different periods of operation. The present study analyzes the spatiotemporal variation in transit accessibility and proposes a modification to enhance the evaluation process. The proposed modification involves assigning weighted indexing to the public transport coverage index (PTCI) using the CRITIC (criteria importance through intercriteria correlation) MCDM technique. The indicators exhibit temporal and spatial variations based on network and operational characteristics, with temporal variations relying on the number of scheduled transits and spatial variations influenced by the network and other operational attributes. The case study conducted in Surat, India, reveals that areas such as the city center and inner fringe have a higher concentration of scheduled transits and bus stops. However, demand fulfillment, measured by the offered seat capacity per population, is relatively low in most zones. To prioritize areas for resource allocation and policy implementation, the use of “RAdial REferenced Scatter QUAdRant (RARE SQUARE) Performance” charts are developed, which provide a straightforward tool to validate findings. The study highlights a low relative transit demand in the city, resulting in a mode share of approximately 2.5%.

Real-time automatic displacement monitoring of metro tunnels is vital for ensuring operational safety and contributes to carbon reduction goals by improving system efficiency. This study focuses on key monitoring elements such as displacement, settlement, convergence, and cracking. Through the analysis of continuous monitoring data, a real-time displacement monitoring model for metro tunnels based on robotic total stations is proposed. This model can timely identify potential risks, thereby ensuring the safe operation of tunnels and reducing carbon emissions from unnecessary maintenance operations, thereby reducing the carbon footprint of metro operations. This article takes the Jinan Metro Tunnel Displacement Real-time Monitoring Project in China as a case study and constructs a comprehensive monitoring framework using robotic total stations, intelligent automated deformation monitoring data collectors, and cloud servers. The implementation details of the project, displacement monitoring principles, monitoring system construction, and data analysis processes are elaborated in detail. Taking the monitoring data of Jinan Metro Line 2 from April 1, 2022, to May 31, 2023, as an example, the results show that the tunnel displacement is within the safe range, verifying the practical application value of the method proposed in this paper. It can effectively ensure the safe operation of the metro and promote sustainable development and low-carbon metro construction.

Travel Time Reliability (TTR) plays a pivotal role in commuting. Nevertheless, existing measurement methods are not specifically designed for commuting scenarios, and their direct application to assess TTR for commuting may yield results incongruent with actual commuting conditions, as they overly rely on measures like mean and percentiles. Drawing on the cyclical characteristics of commuting, the study has established a TTR measurement model based on information entropy and standard deviation, tailored to individual commuters. By selecting commuting data from extensive travel datasets and applying both this model and conventional measurement methods, the focus is on quantitatively analyzing TTR for metro commuters and car commuters under various feature conditions, with a particular emphasis on commuting to work. The objective is to verify the feasibility and advantages of the proposed model. The research indicates that, compared to typical measurement methods, this model more accurately reflects TTR for commuting purposes. The results underscore a significantly superior TTR for metro commuters over car commuters. Distance and departure time exert a substantial impact on the TTR of car commuters, while distance and transfer times moderately influence the TTR of metro commuters. These findings serve as a crucial foundation for enhancing the quality of commuting experiences.

Vehicle interactions with different driving behaviors in mixed traffic conditions, in which autonomous vehicles (AVs) and manual vehicles (MVs) coexist, would result in unstable traffic flow leading to a potential crash risk. A proactive traffic management strategy is required to enhance both safety and mobility by preventing hazardous events in connected environments. The purpose of this study is to develop a Proactive Lane-changE Assistant Strategy for Automated iNnovative Transportation (PLEASANT) to enhance traffic safety. PLEASANT is a strategy for providing lane change assistance information to vehicles approaching risky situations such as crashes, broken vehicles, and upcoming hazardous obstacles. In addition, this study proposed a comprehensive simulation framework that incorporates driving simulation and traffic simulation to evaluate the performance of PLEASANT when dealing with mixed traffic. To characterize vehicle interactions between AVs and MVs, this study analyzes driving behavior in mixed car-following situations based on multiagent driving simulation (MADS), which is able to synchronize the space and time domains on the road by connecting two driving simulators. The characteristics of vehicle interactions between AVs and MVs were incorporated into microscopic traffic simulations. The effectiveness of PLEASANT was evaluated based on the crash potential index from the perspective of safety. The results showed that PLEASANT was capable of enhancing traffic safety by approximately 21%. PLEASANT is expected to be useful as a novel management strategy for enhancing traffic safety in mixed-traffic environments.

Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain.

The ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected-Automated Vehicles (CAVs). Based on the five-order polynomial trajectory planning for CAVs, the 2D-Action Asynchronous Lane Change (AALC) trajectory planning model is constructed by further considering the longitudinal and lateral driving action execution time parameters. This is done to improve the applicability of the lane change model and increase the CAV lane change success rate. The continuous collision space algorithm is constructed by determining the continuity condition of collision trajectory parameter solution space through the monotonicity of trajectory curve parameters and collision form classification. AALC trajectory safety judgment is realized through this algorithm. A cooperative lane change trajectory evaluation objective function is constructed, considering multivehicle comfort and efficiency. Finally, the AALC model is solved in the continuous collision space according to the optimal objective function, and the lane change is divided into free, cooperative, and refused according to the optimization. The results indicate that the AALC model achieves the transfer of collision space between lanes through asynchronous process of behavior execution time window, thereby reducing the possibility of vehicle collision. The AALC model reduces the degree of change of cooperative lane change parameters by asynchronous process of behavior, increasing the number of free lane change trajectories by about 17%, effectively reducing the occurrence of lane change refusal, improving the successful rate of lane change, and enhancing the overall evaluation of the lane change. The AALC model realizes the reallocation of collision space between different lanes through asynchronous process, making it more suitable for environments with large differences in vehicle gaps such as ramp merging. The collision-based trajectory optimization algorithm can quickly obtain the corresponding safety space and optimal trajectory. The maximum calculation time for a single cooperative lane change is 0.073 s, thus enabling real-time trajectory planning.