Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Development of a Retrofit End Enclosure for Enhanced Light-Rail-Vehicle Collision Safety with Automobiles
No full textA prototype end enclosure, or bumper, was designed to retrofit the Siemens SD660 LRV (light-rail vehicle). The objective of the design was to reduce potential for injury to automobile occupants and damage to the LRV car body, and to lower costs to operators from crashes. The bumper was designed using nonlinear dynamic finite-element analysis. Side and oblique impact simulations were performed against a small car (2010 Toyota Yaris) and an SUV (2003 Ford Explorer). Injuries caused by collision were evaluated using a model of the ES-2re Side Impact Dummy (SID). Injuries were calculated for the head, chest, abdominal area, and neck using the abbreviated injury scale (AIS). Simulations were performed for LRV impact speeds of 20 mph against the automobiles. For this speed, the bumper is designed to remain usable in service. Adding the bumper to the collision interface significantly reduced the potential for serious injuries in all the collision scenarios evaluated. For the 2003 Explorer, injuries were reduced from an AIS3+ (serious) chest injury probability of 48.5% without bumper to 21.8% with the bumper when considering normal (90°) side impact. For the 2010 Yaris, injuries were reduced from 100% AIS6+ (fatal) injury probability, resulting from head impact against the LRV anticlimber, to 12.4%. The bumper was also designed to be functional and remain in service for LRV-to-LRV crash speeds of 5 mph. To protect against LRV collisions at higher speeds, the bumper side panels break away at 11 mph, and the existing LRV crash-energy-management performance is unaffected
Bridging Artificial Intelligence and Railway Cybersecurity: A Comprehensive Anomaly Detection Review
No full textRecently, the techniques of industrial control systems (ICS) have developed rapidly, which leads to new cyber threats in this field. The railway system, as a special ICS, is also facing more and more challenges in the intrusion detection and risk evaluation fields. However, compared with other ICS, the intrusion detection and defense methods for railway systems are lagging behind. This paper is a comprehensive review of the application of artificial intelligence (AI) in the railway industry, with a particular focus on cybersecurity. We examine existing anomaly detection methods based on AI and their implementation in ICS and railway operations. We found that machine learning and deep learning algorithms are effective in processing large amounts of network traffic data, modeling normal system behavior, and detecting anomalies. Different AI-based anomaly detection algorithms each have their own strengths and weaknesses, and they hold significant potential for enhancing the cybersecurity of railway systems. While the field of AI in the railway industry is still in its early stages, several case studies demonstrate that AI technologies have already shown considerable promise in safeguarding railway networks. However, there are still numerous challenges in practical applications, such as improving accuracy, generalizability, and robustness. Addressing these challenges will be critical for realizing the full potential of AI in railway cybersecurity and ensuring the safety and efficiency of railway operations in the future. Our work serves as a guide for future explorations, aiming to contribute to the broader discourse of AI applications in industrial cybersecurity
A data-driven MPC approach for virtually coupled train set with non-analytic safety distance
No full textResorting to the emerging virtual coupling technology, multiple train units can operate as a virtually coupled train set (VCTS) to improve the flexibility and efficiency of train operations. To strictly guarantee collision avoidance, the space–time separation principle should be employed, where a non-analytic safety distance is used to safely separate units among VCTS. Thus, the formation control of VCTS is challenging, since it lacks analytic models to tune controllers with tracking accuracy and computational efficiency. To solve this problem, this paper proposes a data-driven model predictive control (DDMPC) approach. Based on a database with previously measured VCTS trajectories, we present a linear data-driven model to describe the non-analytic VCTS formation, such that the controller of DDMPC is yielded by solving a quadratic programming problem in a computationally efficient way. Next, to improve tracking accuracy, we optimize the modeling accuracy in the cost function of DDMPC, and bound the uncertainties from data-driven modeling and coupled states of VCTS. Furthermore, sufficient conditions are derived to guarantee constraint satisfaction and stability for VCTS. Finally, the advantages of the proposed DDMPC approach are demonstrated by comparing with several approaches in tracking accuracy and computational efficiency
Energy-efficient multi-curve optimization in urban rail transit: Stability enhancement under operational uncertainties and curve adjustments
No full textThe multi-curve optimization problem involves selecting train speed curves for nominal timetables and configuring candidate curves embedded in the Automatic Train Operation (ATO) system for train rescheduling. In practice, train speed curves planned under nominal conditions are frequently disrupted by uncertainties such as delays and fluctuations in passenger flow, which may require rescheduling, where the actual speed curves can only be selected from the candidate train speed curves. This rescheduling process leads to deviations between rescheduled (actual) and nominal energy performance. Existing research has not fully addressed the impact of rescheduling on energy consumption from a planning perspective, a critical gap for improving the efficiency of energy-efficient timetables under uncertainty. To fill this gap, we define the stability of energy-efficient train timetables as a quantifiable metric, assessing deviations in terms of both energy reduction and delay control. To minimize actual energy consumption, this study incorporates stability-based constraints into a two-stage stochastic programming model, combining an energy-efficient scheduling stage with a bi-level programming stage for speed curve rescheduling, which introduces nonlinear complexities. Two logic-based Benders decomposition algorithms, including a novel multi-scenario dynamic programming method, solve the model. Using actual data from the Beijing Yizhuang Line, we conducted two sets of numerical experiments to validate the performance of the model and algorithms. Compared to a benchmark two-stage model without optimizing the candidate train speed curves, our approach achieves average stability improvements of 2.74% for in-sample tests and 2.40% for out-of-sample tests, with gains surpassing 4.00% under more challenging delay scenarios, alongside reductions in energy consumption
Time Matters: Analyzing the Impact of Nighttime on Commuter Trip Chaining Behavior and Travel Time Use in Karlsruhe, Germany
No full textTrip chaining and the use of travel time by commuters have long been the foci of considerable research interest. However, despite this attention, few studies have investigated the differences in behavior associated with daytime and nighttime travel. To fill this gap in the literature, the present study investigated the influence of time of day on trip chaining and travel time use. The analysis was based on a data set of residents of the Karlsruhe area who regularly commute both during the day and at night. The data set was analyzed both descriptively and through the application of a series of logistic regression models. The results indicated that nighttime had a significant influence on the formation of trip chains and the use of time during the commute. For example, both the number of trip chains formed and the number of activities performed during the commute are reduced at night. The reasons for forming trip chains also varied according to the time of day. Whereas most trip chains were formed for errands during the day, at night, the majority were formed to pursue leisure activities. In addition, activities such as working, telephoning, looking at the landscape, or reading were significantly reduced at night. The findings of this study contribute to a more in-depth understanding of commuter behavior, on the basis of which, innovative and efficient mobility strategies can be further developed
Two-step Passenger-to-Train and Route Assignment Model for Urban Rail Transit
No full textAccurate passenger flow assignment results are crucial for the operational organization of urban rail transit. However, because of barrier-free transfer, the transfer information of passengers is not recorded. The paper proposes the two-step passenger-to-train and route assignment (TPTRA) model to address this issue. Each passenger is assigned to the trains and one route considering the complex transfer situation using TPTRA compared with the existing passenger-to-train model. Constraints on passengers, trains, and routes are constructed based on a large amount of real automatic fare collection data, train schedules, and metro topology networks to improve accuracy and transferability over traditional survey-based methods. The generalized logistic is first used to evaluate the egress time, access time, and transfer time distributions. Finally, TPTRA is developed based on the constraints and distributions. The model estimates the probability of passengers choosing the feasible routes and boarding the first train. It also calculates transfer passenger flow values and the probability of passengers being left behind. The analysis demonstrates a notable enhancement in the accuracy of the egress time distribution fitting using the generalized logistic distribution, with an improvement of 84.41% compared with the lognormal distribution and 15.78% compared with the normal distribution. Furthermore, the model’s effectiveness is validated through sectional passenger flow comparisons, yielding R-squared values between 0.847 and 0.902, indicating the effectiveness and reliability of the TPTRA model
Optimal charger deployment for electric buses: Incorporating en-route charging and battery management
No full textThis paper presents a mixed-integer nonlinear programming aimed at optimizing the deployment of pantograph chargers for electric buses at stops. The model addresses critical real-world factors such as battery capacity fading, operational uncertainties, and fleet scheduling. The primary goal is to minimize the total operational cost of transit system, encompassing bus acquisition and battery deterioration costs, while adhering to a combined budget constraint for charger deployment. A case study based on an actual bus line is conducted to evaluate the model’s practical applicability. The numerical analyses demonstrate that incorporating en-route charging enables a reduction in the fleet size of electric buses to a level comparable with traditional buses, and concurrently, it significantly decreases battery degradation cost by up to 12.6%. These findings highlight the substantial economic benefits of integrating en-route charging into electric bus operations, particularly in densely populated urban areas with extensive bus networks
Quantifying variable contributions to bus operation delays considering causal relationships
No full textBus services often face operational delays due to dynamic conditions such as traffic congestion, which can propagate through bus routes, affecting overall system performance. Understanding the causes of bus arrival delays is crucial for effective public transport management and control. Moreover, understanding the contribution of each factor to bus delays not only aids in developing targeted strategies to mitigate delays but is also crucial for effective decision-making and planning. Traditional research primarily focuses on correlation-based analysis, lacking the ability to reveal the underlying causal mechanisms. Additionally, no studies have considered the complex causal relationships between factors when quantifying their contributions to outcomes in public transport. This study aims to analyze the factors causing bus arrival delays from a causal perspective, focusing on quantifying the causal contribution of each factor while considering their causal relationships. Quantifying a factor’s causal contribution poses challenges due to computational complexity and statistical bias from the limited sample size. Using a causal discovery method, this study generates a causal graph for bus arrival delays and employs the causality-based Shapley value to quantify the contribution of each variable. The study further uses the Double Machine Learning (DML) approach to estimate the causal contributions, which provides a consistent and computationally feasible method. A case study was conducted using Google Transit Feed Specification (GTFS) data, focusing on high-frequency bus routes in Stockholm, Sweden. To validate the model, cross-validation was performed by comparing variable importance rankings with traditional models, including Linear Regression (LR) and Structural Equation Modeling (SEM). The comparison shows that results from the causality-based Shapley value significantly differ from those obtained by traditional methods in terms of importance rankings and influence magnitudes. The findings underscore the significant impact of origin delays on bus punctuality, a factor often underestimated in previous studies. Additionally, it demonstrates that employing a causal discovery model can not only infer causal relationships but also reveal direct and indirect effects, which can provide more intuitive explanations. Finally, although the causal results are mathematically and intuitively sound, it is important to further investigate the real causality impact in practice using lab experiments or A/B tests in real-world settings
What drives inequalities in Low Emission Zones’ impacts on job accessibility?
No full textLow-emission zones (LEZs) aim to improve urban air quality and reduce emissions but often face public opposition due to their regressive impacts on accessibility. However, the causes of these regressive impacts remain poorly understood. This study investigates the factors driving inequalities in the impacts of LEZs on job accessibility across occupational categories in eight French cities. Using ex-ante open-source data, it computes expected job accessibility losses due to LEZs per occupational category. Additionally, it provides a counterfactual decomposition of the disparities in LEZs’ impacts between six drivers: ownership of polluting vehicles, workers’ residences and workplaces within the LEZ, accessibility of workers’ homes and workplaces via public transportation, and feasibility of active transportation modes for commuting between homes and workplaces. The findings reveal that LEZs are predominantly regressive in six out of the eight cities examined. Despite a higher concentration of high-income workers and jobs within LEZs, resulting in significant accessibility losses for this group, low-income workers bear a greater burden due to the limited availability of public transportation near their residences and workplaces, longer commutes to work, and higher shares of polluting vehicles. These findings help inform potential complementary policies to address the regressive effects of LEZs
Optimal coordination of electric buses and battery storage for achieving a 24/7 carbon-free electrified fleet
No full textElectrifying a commercial fleet while concurrently adopting distributed energy resources can significantly reduce the cost and carbon footprint of its operation. However, coordinating fleet operations with distributed resources requires an intelligent system to determine joint dispatch. In this paper, we propose a 24/7 Carbon-Free Electrified Fleet digital twin framework for the coordination of an electric bus fleet, co-located photovoltaic solar arrays, and a battery energy storage system. The framework optimizes electric bus and battery storage operations to minimize costs and emissions with the consideration of on-site solar generation, hourly marginal grid emissions factors, and predictions of bus energy consumption through a surrogate model. We evaluate the framework in a case study of Stanford University’s Marguerite Shuttle electric bus fleet for both a campus depot, whereby non-controllable loads are coupled behind-the-meter, and a stand-alone depot. In a techno-economic analysis, we find that joint optimization of a campus depot’s battery storage and bus operations saves at least 66 USD/tCO compared to a model that only minimizes electricity costs. Furthermore, optimized bus and battery operations have even greater impact in reducing electricity costs under new net billing tariff policies (“net energy metering (NEM) 3.0”) compared to previous NEM 2.0 policies. As adoption of electric buses continues to grow, fleet operators may leverage our flexible framework to ensure smart, low-cost, and low-emissions fleet operations