Railways are essential to socio-economic development, serving as a critical piece of infrastructure that requires continuous monitoring and upkeep to maintain safety and reliability. Traditional approaches have largely depended on manual inspections and maintenance, necessitating direct physical interaction with railway infrastructure for its installation, maintenance, and repair. Yet, recent advancements in technology have opened the door to more sophisticated, data-driven methods that enhance the safety and precision of railway maintenance. This paper presents a comprehensive approach for monitoring railway tracks and identifying and classifying anomalies in track gauge. By applying a sequence of nonlinear transformations to LiDAR scan data, we align it within a cohesive coordinate system for precise track gauge measurement. We propose an algorithm for detecting abnormal gauges, leveraging both global and local detection techniques. The findings from our case study affirm that the methodology outlined in this study is capable of accurately identifying both global and local gauge irregularities, offering automated classification and indepth analysis of these discrepancies. This significantly supports the maintenance and operational management of railway systems.

Optimising driving velocity profiles is crucial for reducing vehicle fuel consumption and NOx emissions without altering core vehicle components. While many studies have addressed eco-driving, most have focused solely on minimising fuel consumption or have treated NOx emissions separately, resulting in distinct, non-integrated speed profiles, and have often neglected the influence of real-time traffic. To overcome these limitations, this paper introduces a novel Multiobjective Speed Profile Optimisation (MO-SPO) framework for eco-driving that simultaneously minimises fuel consumption, NOx emissions, and travel time while accounting for surrounding traffic. Two solution approaches are developed and compared: a two-phase Model Predictive Control (MPC) method and a newly proposed Deep Reinforcement Learning (DRL) method that directly integrates multiple objectives and real-time traffic constraints into the speed control policy.Simulation results on a UK highway segment, with vehicle dynamics and engine characteristics derived from GT-SUITE data, demonstrate the benefits of the proposed framework. For instance, at one representative Pareto point, results indicate that the DRL approach achieves up to 10% lower fuel consumption and 16% lower NOx emissions compared to MPC-based methods while reducing travel time by approximately 5%. In addition, the DRL method maintained safer headway distances, offering more robust eco-driving strategies in dynamic traffic environments.This work is the first to apply multiobjective optimisation to generate integrated speed profiles that consider fuel, NOx, and travel time simultaneously under realistic traffic conditions.

As vehicle exhaust regulations become more stringent, non-exhaust particulate matter (PM) emissions, particularly from brake wear, which accounts for up to 55% mass of these emissions, have become major contributors to traffic-related PM. However, how low-emission driving behavior influences brake wear PM emissions in real-world conditions remains unclear. In this study, we developed a low-emission driving assistance application and, for the first time, evaluated the real-world impact of low-emission driving behavior (LEDB) on brake wear PM2.5 and PM10 emissions. LEDB training was implemented for volunteer drivers in Leeds and Helsinki, resulting in average reductions in brake wear PM2.5 emissions by 22.8% and PM10 emissions by 26.1%. Additionally, the promotion strategies for LEDB training are discussed, and the expected emission reduction effects across different implementation scenarios are analyzed. These findings demonstrate that LEDB represents a promising and cost-effective approach that could contribute to reductions in brake wear emissions and improved air quality.

Governments worldwide are considering banning the sale of internal combustion engine vehicles (ICEVs) to address environmental and public health issues. However, the environmental and health impacts of banning ICEV sales remain unclear. Here, we evaluated the effects of banning ICEV sales under various implementation timelines on PM2.5 and NOx emissions in Leeds, UK, and the associated premature deaths, using a novel ’Fleet-Emission-Health’ model. The results showed that the earlier ban on ICEV sales led to an almost 100% reduction in NOx by 2040, whereas total PM2.5 increased slightly due to more non-exhaust PM2.5 emissions emitted from battery electric vehicles (BEVs). Moreover, banning ICEV sales in 2030 would avoid 28 deaths in Leeds in 2040 compared to those in 2022, due to the reduction of NOx and PM2.5 emissions from ICEV exhaust. The findings indicate that stricter regulations on non-exhaust emissions are necessary to mitigate environmental and public health effects.

Time-varying demand distribution (TDD) is a critical input data for operation and management in HSR systems. This paper proposed a bi-level model to estimate the TDD with the ticket booking date and using the schedule-based User Equilibrium (UE) assignment. The up-level aims to determine the TDD with maximum entropy value and minimal error between the path flow (ticket booking volumes) and the corresponding equilibrium flows (determined from lower-level); the lower-level is a schedule-based UE assignment with rigid capacity constraints to reflect the interactions of ticket booking choices behaviors between different OD pairs in the HSR networks, and further, the advance booking cost is considered endogenously as a part of passenger choice equilibrium. The bi-level model is converted into a single-level model through equivalent complementary constraints. Then, based on linear relaxation, the single-level model is transformed into a mixed-integer quadratic program (MIQP). Furthermore, in order to improve the computational efficiency of the MIQP, the approach of reducing the calculation size of our problem is proposed. By solving the MIQP we get the information about the upper and lower bounds of our original problem, and then a global optimal solution algorithm with four piecewise interval strategies is proposed. The effectiveness and applicability of the proposed algorithm are illustrated with a simple case and three real-world cases.

Liability attribution for crashes involving automated vehicles (AVs), if applied improperly, is a factor which can potentially hinder acceptance. The present study investigated the impact of liability attribution on intention to buy an AV. A vignette-based survey was implemented with a hypothetical crash similar to the 2018 Uber crash (which was jointly caused by driver distraction and the malfunctioning of the automated system) leading to a pedestrian's fatality. Respondents (N = 1524) chose their preferred liability attribution, ranging from human driver exclusively liable to AV manufacturer exclusively liable. Respondents were then randomly allocated to different conditions of actual liability attribution by the local authority. These conditions were then combined into, negative misattribution (the authority assigned more liability to the human driver, compared to the respondent), positive misattribution (the authority assigned less liability to the human driver), and no misattribution. Negative misattribution negatively affected intention to buy; however, positive misattribution did not have a significant impact. The results of a multiple-mediator model indicated that negative misattribution affects intention to buy through the mediating effects of trust, negative affect, and crash acceptability. Theoretical and practical implications of our results are discussed.

In large-scale disasters, evacuation using public transport, such as buses, is always essential, especially with limitations on the time window and resources available. Unfortunately, a Bus Evacuation Problem (BEP) is NP-hard in nature. This means it is impossible to find an optimal evacuation plan within a reasonable time window. Therefore, it is efficacious and requisite to use an approximation algorithm such that a sub-optimal plan can be derived quickly in large-scale disasters. In the literature, an approximation algorithm for BEP has been presented. However, there is not only a lack of rigorous proof of the approximation ratio, but also the computation time of the approximation algorithm is influenced by the number of evacuees. In this work, firstly the approximation ratio is derived as 2nb+7, where nb is the number of buses, which is fundamentally more precise than the existing work. Further, the approximation ratio can be reduced to nb+6 through a new proof provided in this work. More importantly, a new approximation algorithm is proposed in this work, with an aggregated network flow model that can be quickly solved as a linear programming problem in polynomial time. The approximation ratio of the new algorithm is proven to be nb+1 when there is a pile of buses, and the computation time is insensitive to the number of evacuees. Finally, 10 sets of random cases and a real-life disaster, the flooding in Xingguo, China in 2019 are studied to illustrate the efficiency and practical applicability of the proposed approximation algorithm.

In large-scale disasters, as an important part of the rescue management, buses are often used to evacuate carless people. Timing is always the main concern in any evacuation planning. Hence, not only is minimizing evacuation time the primary goal of bus evacuation problems (BEPs), but computation time to solve a BEP is also crucial. Nevertheless, BEP is NP-hard which makes optimization of the BEP for large-scale disasters within an acceptable time intractable. Practically, sub-optimal but efficient algorithms for bus evacuation planning are desired. In this work a two-step sub-optimal algorithm, called network flow planning (NFP) algorithm is proposed. In the NFP, firstly, an aggregated network flow model, which minimizes the total travel time of all the buses, is adopted. It is proven that the model can be solved as a linear programming problem. The minimum total travel time solution is then converted to approximate the minimum evacuation time solution by assigning evacuation tasks to all buses as equally as possible in the second step. To verify the effectiveness of the NFP algorithm, a greedy algorithm inspired by NFP is presented, and several numerical case studies are presented in this paper. In a Monte Carlo simulation study, randomized cases are used to demonstrate the superiority of the proposed algorithm over the presented greedy algorithm, a genetic algorithm, an approximation algorithm and CPLEX. Furthermore, a real-world large-scale flood disaster case in Xingguo, China is studied, which illustrates the efficiency and practical value of the proposed algorithm in large-scale evacuations.

PM10 emissions generated from the brake wear of passenger car per braking event during three test driving cycles (WLTP, LACT, and WLTP-Brake) were studied using a finite element analysis (FEA) approach in combination with the relationship among the mass emitted rate of airborne particles versus local contact pressure and sliding speed. In addition, PM10 emissions were measured per braking event during the WLTP-Brake cycle on a brake dynamometer using an electrical low-pressure impactor (ELPI+) to validate the proposed FEA approach. The simulated and experimental results for WLTP-Brake illustrated that the proposed simulation approach has the potential to predict PM10 from brake wear per braking event, with an R2 value of 0.93. The FEA results of three test driving cycles showed that there was a gradient rise in pad wear on both sides from the inner to outer radii. The simulated PM10 emission factors during the WLTP, LACT, and WLTP-Brake were 7.9 mg km-1 veh-1, 9.8 mg km-1 veh-1, and 6.4 mg km-1 veh-1, respectively. Among three test driving cycles, the ratio of PM10 to total brake wear mass per braking event was the largest for the LACT, followed by WLTP and WLTP-Brake. From a practical application perspective, reducing the frequency of high-speed braking may be an effective way to decrease the generation of PM10 emissions.

PurposeThis paper aims to propose a medium-term forecast model for the daily passenger volume of High Speed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume for multiple consecutive days (e.g. 120 days).Design/methodology/approachBy analyzing the characteristics of the historical data on daily passenger volume of HSR systems, the date and holiday labels were designed with determined value ranges. In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double Layer Parallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of the daily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result by weighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of daily passenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume to ensure the accuracy of medium-term forecast.FindingsAccording to the example application, in which the DLP-WNN model was used for the medium-term forecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the average absolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP) neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalized regression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for the medium-term forecast of the daily passenger volume of HSR.Originality/valueThis study proposed a Double Layer Parallel structure forecast model for medium-term daily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and Wavelet Neural Network. The predict results are important input data for supporting the line planning, scheduling and other decisions in operation and management in HSR systems.

现代有轨电车运行过程中,交叉口公交信号优先策略能够保证列车顺利通过交叉口,但会对交叉口其它社会车辆产生负面影响.本文考虑信号优先策略对交叉口产生的负面影响,提出了综合优化列车旅行时间和交叉口负面影响的有轨电车时刻表优化模型,针对模型的非线性特点,设计模拟退火算法和基于事件驱动的仿真法相结合的组合算法进行求解.以沈阳浑南现代有轨电车5号线为实例,结果表明,模型求解的时刻表方案相对于总旅行时间最小的时刻表方案,其旅行时间增加了1.2％,但交叉口的负面影响费用相对减少了66.4％.由此说明,本文提出的现在有轨电车时刻表优化方法,在保证列车旅行时间尽量小的同时极大降低了对交叉口社会车辆的负面影响.

针对跨局零散货物运输,在管内零散货运班列稳定开行的基础上,将其转化为以局中心站为网络主要集散点的跨局列车开行方案的优化问题.在新的运输条件下,分析中转货物的接续等待时间与最大暂存量;提出基于理想运输状态下的运输时效性损失,结合班列循环车底运行的空驶费用、较低列车开行频率下货物流失的收益损失构建模型的目标函数;考虑既有设施设备条件与零散货物运输市场的竞争需求,实现班列开行方案与货物中转方案的协同优化.通过算例证明了该模型的有效性.

Passenger demand plays an important role in railway operation and organization, and this paper aims to estimate passenger time-varying demand by simulating the ticket-booking process for High Speed Rail (HSR) system. The ticket-booking process of each OD pair can be partition into discrete booking phases by the times when the tickets of any itinerary had sold out. The ticket booking volume of each itinerary is reversely assigned to its corresponding expected departure intervals to obtain the time-varying demand in each booking phase using the rooftop model, and the total time-varying demand are estimated by summing the time-varying demand distributions in all booking phases. Only with the data about the itinerary flow, the precedence relationship is introduced to constrain the ticket sold-out order of all itineraries for each OD pair. Based on the precedence relationships of itineraries, two typical situations are proposed, in which the Single Booking Phase Reverse Assignment (SBPRA) algorithm and the Multiple Booking Phases Reverse Assignment (MBPRA) algorithm are proposed to estimate the time-varying demand respectively. Case analysis on OD pair Beijing-Shanghai are presented, and the validity analysis demonstrates that the error rates of SBPRA algorithm and MBPRA algorithm are 8.64% and 6.37%, respectively.

当需要进一步明确铁路技术规章的具体含义或需要明确新情况的适用规章依据时,制定者应作出正确解释,才能保证铁路技术规章被正确理解和执行.目前铁路行业对技术规章解释方法、原则等方面的规定较少,为达到全面规范铁路技术规章解释的目的,应研究补充、完善铁路技术规章解释方面的规定.通过阐述铁路技术规章解释现状,针对铁路技术规章解释存在准确性和公平性有待提高、解释权威有待确立、解释程序有待增加、解释原则和解释方法有待完善等问题,提出明确解释主体、明确解释效力、规范解释程序、遵循解释方法等方面的对策.

Research on fast search algorithm of transfer scheme for railway passengers can provide supports to ticket booking system.Through analyzing characters of rail passenger travel paths,this paper designs decision conditions of an effective path by non-shortest coefficient and develops a search algorithm for effective paths. Based on the set of effective paths,a fast search algorithm of transfer scheme with minimum cost at any expected departure time is proposed by a front-arc-number method.The whole algorithm in this paper includes two parts:firstly,producing and storing effective paths of the whole railway network in advance;secondly,searching a transfer scheme with minimum cost on effective paths with the given departure time and O-D pair.This algorithm captures passenger’s travel characters and lights the fast-search feature.Numerical experiment is conducted based on China high-speed railway network and train schedule in 2014,and results verify the effectiveness and utility of the algorithm.

In traditional OD survey, too many manpower and material resources are consumed, but the life cycle of the data is shortening and survey cost is increasing. In tackling these questions, the paper put forward the theory of calculating flow of road segments by mobile navigation software, get all the traffic link flow in current city after fuzzy process, finally obtain estimate OD data. Analysis Nanchang main city zone, adopt STOCH algorithm, stochastic user equilibrium assignment and system optimum realize OD matrix estimation. The results of the example show that OD matrix estimation based on mobile navigation data has feasibility, which also large scale reduce the OD investigation cost at the same time improve the effectiveness of the OD data.

Due to the passenger line has high number of the train density and train speed,its requirements for line is very high.Rail scratch could bring rail breaking accidents,which affect the safety of the line,and there-fore proposed a scientific and rational approach to guide the site to find out the reason causing abrasions rail is important.This paper presents a method to find out the reason of rail scratch,which contains flat vertical section line survey,transport organization pattern analysis,locomotive type selection analysis.Using the proposed meth-od to analyze the rail scratch,case study shows that analytical method of rail scratch presented in this paper was realistic and feasible,and it had a positive effect on finding out the reasons for rail scratch for field work.