Long-distance passenger lines exceeding 800 kilometers are a crucial component of highway passenger transport. This paper analyzes the causes of traffic accidents on long-distance passenger routes exceeding 800 kilometers from 2014 to 2020. This paper conducts a causal analysis and researches accident-prone scenarios for long-distance passenger lines exceeding 800 km. It selects key characteristics to establish an index system based on human factors, road conditions, and the environment. This paper analyzes the characteristics of humans, roads, and the environment in the traffic system when accidents occur. The Decision Tree model is utilized to investigate the internal relationships among key factors in accident scenarios, such as operating time, human factors, weather, quarter, accident region, holidays, spring transportation, road alignment, and road condition. The results show that the run time, human factors, and weather conditions are important influencing factors for accidents on long-distance passenger lines over 800 km. They have a coupling relationship with factors such as quarter, accident region, road alignment, and road condition. Holidays, spring transportation, highway technical grade, and highway administrative grade are not the important factors affecting the occurrence of accidents on long-distance passenger lines over 800 km. This paper provides a theoretical basis for reducing the potential for accidents and promoting safety within enterprises operating long-distance passenger lines over 800 km. In the future, road transport management departments and road transport enterprises should strengthen safety management, standardize drivers’ safe driving behavior, and promote defensive driving knowledge in accident-prone situations.

Under the guidance of the "Internet Plus" policy, China’s logistics industry has experienced high-speed growth in recent years. However, due to the exposure of existing problems, consumers and operators are faced with new challenges, focusing on high distribution costs, slow logistics, high complaint rates, fewer rural distribution points, and so on. In particular, solving the last-mile problem in the new era for various objectives such as implementing the rural revitalization strategy, enhancing people’s satisfaction and happiness, and achieving common prosperity are urgent challenges that we must address now. Based on the analysis of the current situation in China’s logistics industry and by referencing express distribution modes in foreign countries, this paper analyzes the bottleneck problems that challenge last-mile delivery. It proposes targeted solutions from five perspectives: effective planning and tracking software, development of the delivery system, agile logistics status updating, promotion of technological innovation, and enhancement of the overall ability and efficiency of local logistics services.

This study aims to investigate the multi-modal travel behavior and obtain quantitative results for various indicators by building an eqasim/MATSim model, using Shanghai as the study area. Travel demand is mainly generated using mobile phone signaling data. For each mode, a travel cost model is formulated. Additionally, an MNL (Multinomial Logit) model is integrated into eqasim through the DMC (Discrete Mode Choice) module. The results demonstrate that using mobile phone signaling data to generate travel demand yields a high-quality representation of travel demand. Users prefer public transport over cars when travel distances are similar. Furthermore, for longer-distance travel, the combined bus and subway mode significantly reduces walking distance, travel time, and travel costs. The spatial accessibility of public transport strongly depends on the availability and coverage of the public transport infrastructure. In areas where public transport services are limited, cars can complement public transport by providing accessibility to areas with scarce public transport options. From a transportation system perspective, car trips during rush hours are similar to public transport and biking, while walking is consistently used throughout the day due to the shortest travel time. Home-based trips, particularly commuting trips, have the highest share. Understanding these travel patterns is essential for optimizing transportation planning and effectively addressing peak-hour travel demand. This study demonstrates the effectiveness of using mobile phone signaling data for studying multi-modal travel behavior. The results provide valuable insights for transportation planners and policymakers in developing efficient and sustainable transportation systems that meet the preferences and needs of travelers.

With the increasing trend of battery electric vehicles, it is no longer appropriate to use the traditional fuel vehicle’s driving cycle for battery electric vehicle driving cycle research. In order to obtain the driving cycle of the battery electric vehicle and compare it with the traditional driving cycle from a data perspective, the driving data of the battery electric vehicle in the Chongqing area was analyzed. The proprietary big data platform provided whole vehicle and battery data, and the short-stroke method was used to identify the kinematic fragments. Then, the kinematic fragments’ feature is constructed using the correlation method of feature engineering. The dimension of high-dimensional feature data is reduced through principal component analysis, and the feature weight is calculated to eliminate the influence of feature correlation. Next, the K-means++ clustering method is used to partition the structure of the driving speed and battery current curves, and thus to construct four types of working conditions: low speed, medium speed, medium-high speed, and high speed. Moreover, the most suitable short-time working conditions are selected by sorting the distance of each working condition from the cluster center as the origin. The weight of the data set is determined by the ratio of the total duration of the data set to the total duration of the data set. Through error analysis, the characteristic deviation error of the smallest curve is selected as a representative of the working condition to determine the representative of the vehicle and battery working conditions. Finally, the reliability of the battery electric vehicle operating curve in a mountainous environment is demonstrated by comparing international and domestic typical operating conditions. The results indicate that in mountainous environments, pure electric vehicles exhibit shorter constant speed driving times, longer idling times, greater deceleration, and a smaller proportion of deceleration time compared to non-mountainous environments. Additionally, battery discharge efficiency is higher, and the vehicle speed is maintained at a moderate level.

Addressing the lateral path tracking control issue of autonomous vehicles during data dropout, an improved model-free adaptive control system with data compensation (DC-EMFAC) is introduced. First, the method introduces a dynamic linearization technique with a time-varying factor pseudo gradient (PG) to linearize the dynamic process of an autonomous vehicle, and then designs a model-free adaptive controller. Moreover, addressing the issue of data dropout in the actual system, this paper employs an estimation algorithm to estimate the data loss at the present time based on the system’s input and output (I/O) from the past and PG. The advantage of the DC-EMFAC is that the controller design process is based on the I/O data of the controlled object, without the need for an accurate mathematical model. The effectiveness of the proposed algorithm is verified through a series of simulations on the Panosim platform.

Amid escalating environmental and energy-related challenges, the advancement of electric vehicles has become a pivotal national strategy in China for energy conservation and emission reduction. This study presents an analysis of the methodology used for calculating energy consumption in electric vehicles in comparison to traditional fuel vehicles. The customary practice of assigning monetary value to carbon emissions is integrated to transmute carbon emission reduction methods into fiscal terms. This transformation enhances suitability for evaluating energy transitions and benefit comparisons. Concurrently, data from the New Energy Vehicle Open Laboratory V 2.0 were used to calculate the carbon quota produced throughout the charging and operating procedures of the national new energy public transportation in 2019. Additionally, charging expenses were calculated considering the varying electricity prices at different times of the day, and an assessment was conducted on the reduction of operating costs resulting from the annual benefits of carbon emission reduction. It is evident that, given the current carbon emission factor of China’s power grid, electric buses have successfully transitioned their emission reduction effect from negative to positive.

This article aims to introduce an innovative framework and associated construction content for the modern smart highway, and to succinctly provide viable recommendations for its rapid development. This study examines the construction frameworks and guidelines across 13 provinces and cities, identifying their commonalities and variations. The new framework is categorized into five layers: Perception, Support, Data, Computing, and Application. The construction key points in these layers are concisely explained and the Application layer is further segmented into six facets: Safety, Efficiency, Green, Convenience, and Toll Collection. This study analyzes the critical application scenarios in various provinces and cities. It aids in rectifying the disparity in understanding during actual construction across these provinces, providing a classification criteria for smart highway construction content. These guidelines will significantly influence future project construction improvements.

This study focuses on understanding the deicing salt corrosion mechanism affecting highway roadside concrete structures and identifying appropriate protective measures. Initially, the mechanism of salt corrosion on roadside concrete is elucidated through thorough research and investigation. Subsequently, the effectiveness of protective materials such as acrylic, polyurethane, and epoxy coatings in mitigating salt corrosion in concrete is assessed by simulating salt corrosion conditions on the concrete surface. Optimizing the material formula based on the tests more effectively mitigates the corrosion effect. Results indicate that salt corrosion primarily results from the permeation of salts into concrete, triggering physiochemical corrosion. It is observed that all three types of protective materials significantly decelerate concrete corrosion caused by salt, demonstrating effective anti-corrosion properties. Among these, the optimized formulation of polyurethane coating ’C’ demonstrates superior permeability and resistance to chemical corrosion. It effectively prevents the ingress of salt into the concrete, thereby reducing the occurrence of salt corrosion in concrete. The low cost of these measures indicates their suitability for widespread implementation.