Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Novel Approach for Quantifying the Propagation of Subway Equipment Faults by Using Multimodal Networks
No full textIn subway systems, equipment failures can lead to train stoppages, delays, or even accidents, severely affecting the safety of train operations. It is crucial for emergency handling to identify the vulnerable areas and explore propagation laws in the subway network. In this paper, based on multimodal networks, several indicators are systematically proposed to demonstrate the vulnerability of each subway station. These indicators are then applied in a gravitation model to quantify and study the propagation or spatiotemporal distribution of cascading effects caused by equipment failures. To verify the effectiveness of the proposed model, a case study is conducted using a data set of equipment faults in the Beijing subway. Quantification results show that the propagation of equipment faults between subway stations exhibits diagonal characteristics, with stations closer to the faulty equipment being more heavily affected. The results also indicate that gravitation values display a long-tail distribution and their highest proportion falls within a certain interval. Furthermore, this paper proposes several prevention measures in response to equipment fault propagation
Bi-objective robust nonlinear decision approach for en-route bus speed control considering implementation errors and traffic uncertainties
No full textThis study proposes a bi-objective robust nonlinear decision mapping (Bi-RNDM) approach for en-route bus speed control, aiming to enhance bus service level and reliability. Through a two-stage procedure, the proposed approach addresses the challenges due to traffic flow uncertainties and implementation errors from bus drivers. In the first stage, a bi-objective nonlinear programming model (Bi-NLPM) is built and solved to collect labeled data, which are then used to pre-train the mapping relationship between bus system states and optimal bus control speeds using support vector machines (SVM). This results in a bi-objective pre-trained nonlinear decision mapping (Bi-PNDM) consisting of an SVM-based classifier and an SVM-based regressor. In the second stage, a bi-objective robust critical parameter simulation-based optimization (BRCPSO) model is built within the min–max expectation framework, and it is solved using a modified bi-objective robust simulation-based optimization (MBORSO) algorithm to optimize the critical parameters of Bi-PNDM. The resulting Bi-RNDM improves the operation performance by reducing the deviation in service headway as well as the deviation from service schedule, considering the existence of traffic uncertainties and implementation errors from bus drivers. Numerical experiments are conducted based on the case study of the bus line 406 in Changsha, China, to demonstrate the efficiency of the MBORSO algorithm and the superior bus service level and robustness of the Bi-RNDM method. Results show that the proposed Bi-RNDM method can effectively balance the two competitive objectives, and the produced speed control is implementable for only about 20% of the operation period, suggesting high practicality. The proposed framework is not only applicable in the bus speed control problems, as it promises for addressing other complex multi-objective online optimal decision-making problems that are under various uncertainties and resolvable through data-driven nonlinear decision mapping
Socio-Economic and Spatial Disparity of Bus Ridership Impacts in King County, Washington, During COVID-19
No full textTransit ridership has been seriously affected around the world by the COVID-19 pandemic. This study investigates the impacts of the COVID-19 pandemic on bus service ridership patterns in King County, Washington, using clustering and multinomial logit (MNL) models. Ridership patterns of King County Metro buses during different study periods are detected using clustering. The characteristics of ridership patterns and cluster assignment spatial distributions are further examined. The MNL models were developed using explanatory factors, including socio-demographic, transit service, and land use characteristics at each stop, that are correlated with the ridership pattern cluster assignments. Results of the developed models demonstrate disparities across socio-economic groups and unevenness throughout different neighborhoods in ridership reduction and peaking patterns during COVID-19
Resilience of socio-technical transportation systems: A demand-driven community detection in human mobility structures
No full textExisting scholarship on transportation resilience analysis has primarily focused on engineering resilience, often overlooking the intricate socio-technical dimensions. This oversight underscores the necessity for a more comprehensive understanding of the dynamic interplay between social, including travel behaviors, and technical infrastructure components within transportation systems. This article delves into the impact of “social shocks” on transportation systems, which are defined as disturbances affecting the social subsystem without yet affecting the technical subsystem. Drawing inspiration from C.S. Holling’s ecological resilience, which signifies a system’s ability to cope with change by adapting its structure and functionality, we propose a multi-level resilience assessment framework. It encompasses four mobility-related indicators: entropy (measuring network-level complexity), stationarity (assessing community compositional changes at the cluster level), and two node-level metrics — within-module degree and weighted participation coefficient — capturing location connectivity. These indicators proxy for evaluating the mobility structure and node functionality within the social subsystem. In a case study, we analyze historical smart card data to examine the mobility pattern’s structural changes within Hong Kong, a rail-oriented metropolis, during a prolonged and city-wide protest. The framework and associated indicators provide an alternative perspective for transit planners and operators, allowing them to assess both the overall system and individual stations, moving beyond traditional assessments of service supply and patronage changes
Low emission zone and mobility behavior: Ex-ante evaluation of vehicle pollutant emissions
No full textExposure of the population living in urban areas to an increasing level of air pollution has led local authorities to implement vehicle access restrictions to limit the circulation of pollutant vehicles and foster sustainable travel habits. With these aims, Low Emission Zones (LEZs) have been introduced in several European cities. Many previous works have evaluated the impacts of such regulation; however, they adopted pre-defined assumptions about new travels to access the regulated area and neglected potential behavioral changes induced by the measure. The aim of this paper is to quantify the effects of a LEZ on vehicle pollutant emissions, considering potential short-term variations of travel habits after its introduction (i.e., vehicle replacement, modal shift and destination change), and the associated uncertainty. The study area was the Municipality of Padova (Italy), where a LEZ is likely to be enforced. A holistic evaluation framework was applied combining a behavioral model and a traffic simulation model, calibrated using responses from a mobility survey administered to local stakeholders and traffic counts. The results highlighted the measure could contribute to induce fleet renewal and modal shift toward sustainable transportation means, that could be furtherly fostered by increasing the awareness of the benefits of the LEZ. Furthermore, the outcomes confirmed that the intervention could significantly reduce vehicle pollutant emissions within the area. Nevertheless, a spillover effect could occur outside the LEZ, due to the long detours that travelers deciding to avoid entering the zone have to perform
Free public transport to the destination: A causal analysis of tourists’ travel mode choice
No full textIn this paper, we assess the impact of a fare-free public transport policy for overnight guests on travel mode choice to a Swiss tourism destination. The policy directly targets domestic transport to and from a destination, the substantial contributor to the CO emissions of overnight trips. Based on a survey sample, we identify the effect with the help of the random element that the information on the offer from a hotelier to the guest varies in day-to-day business. We estimate a shift from private cars to public transport due to the policy of, on average, 14.8 and 11.6 percentage points, depending on the application of propensity score matching and causal forest. This knowledge is relevant for policy-makers to design future public transport policies for tourists. Overall, our paper exemplifies how such an effect of natural experiments in the transport and tourism industry can be properly identified
Human-Machine collaborative decision-making approach to scheduling customized buses with flexible departure times
No full textPublic transport agencies need to leverage on emerging technologies to remain competitive in a mobility landscape that is increasingly subject to disruptive mobility services ranging from ride-hailing to shared micro-mobility. Customized bus (CB) is an innovative transit system that provides advanced, personalized, and flexible demand-responsive transit service by using digital travel platforms. One of the challenging tasks in planning and operating a CB system is to efficiently and practically schedule a set of CB vehicles while meeting passengers’ personalized travel demand. Previous studies assume that CB passengers’ preferred pickup or delivery time is within a pre-defined hard time window, which is fixed and cannot change. However, some recent studies show that introducing soft flexible time windows can further reduce operational costs. Considering soft flexible time windows, this study first proposes a nearest neighbour-based passenger-to-vehicle assignment algorithm to assign CB passengers to vehicle trips and generate the required vehicle service trips. Then, a novel bi-objective integer programming model is proposed to optimize CB operation cost (measured by fleet size) and level of service (measured by passenger departure time deviation penalty cost). Model reformulations are conducted to make the bi-objective model solvable by using commercial optimization solvers, together with a deficit function-based graphical vehicle scheduling technique. A novel two-stage human–machine collaborative optimization methodology, which makes use of both machine intelligence and human intelligence to collaboratively solve the problem, is developed to generate more practical Pareto-optimal CB scheduling results. Computation results of a real-world CB system demonstrate the effectiveness and advantages of the proposed optimization model and solution methodology
Safety Evaluation of the First Bus Rapid Transit System in Tanzania
No full textThe first bus rapid transit (BRT) system in Tanzania and the third in Africa became operational in 2016. As it was introduced to relieve congestion and promote efficient means of public transportation, most previous researchers have focused on its effectiveness on travel time reliability. While travel time is important, the safety aspect of the BRT system is equally important. This study conducted a comprehensive safety evaluation of the BRT system in Tanzania using five years (2016–2020) of crash data. The hot spot analysis was conducted to identify locations experiencing more crashes. The text mining approach was used to understand the key themes from crash narratives. The established themes were further analyzed using a Bayesian network (BN) to understand their association with the severity of crashes that involved BRT buses. Analysis of the results showed that major intersections and locations with commercial activities are the hot spots for crashes involving BRT buses. The text network revealed that most crashes involving a BRT bus and pedestrians were associated with drivers failing to obey traffic control devices. Crashes involving a collision between BRT buses and non-motorized road users were more likely to be severe. The BN findings showed that distraction, collision with non-motorized road users, and disregarding traffic control devices are associated with a higher likelihood of severe crashes. The findings are anticipated to assist agencies in improving the safety of existing and future BRT systems
How crowding impedance affected travellers on public transport in the COVID-19 pandemic
No full textIn the aftermath of the COVID-19 pandemic, travel behaviour has changed significantly. Governors have introduced different transport policies to maintain the travel demand in the public transport system. Previous studies have developed the measurement of crowding impedance on public transport to determine the degree of transit use and the impact of the COVID-19 pandemic. This study explores the behavioural differences in crowding impedance to provide transport policies incorporating group segmentations. The D-efficient design process has structured a survey with a reasonable choice set, and questionnaires have been provided to identify the attitudinal groups of travellers effectively. The travellers are divided into four groups according to the values of factor loadings from the factor analysis: i.e., fear of disease, transit preference, time sensitivity, and auto preference. Multinomial logit models explore the behavioural differences in route and mode choices and calculate crowding multipliers. The results show that the group with a fear of disease comprises a high proportion of the elderly owing to their reluctance to expose themselves to infectious diseases; furthermore, the time-sensitive group exhibits less crowding impedance on public transport. Thus, the crowding multipliers differ between the groups and influence the relevant transport policies to promote public transport use. Policymakers are encouraged to introduce customized transport policies depending on the requirements of each group of travellers to cope with the adverse effects of the pandemic
The economics of public transport electrification: When does infrastructure investment matter?
No full textThis paper examines the economic and policy implications of the charging, network and auxiliary infrastructure required for a fully electrified metropolitan Melbourne bus network using overnight depot charging. We introduce the concept of minimum and maximum fleet charging capacities, as well as a charging infrastructure augmented total cost of ownership that is faced by bus fleet owners. Our analysis provides policy makers with a range of viable fleet charging capacities and a cost component breakdown of public bus fleet electrification using battery electric vehicles. We find that a minimum of half the fleet\u27s rated charging capacity is needed to maintain operation, which provides a lower bound for network and charging infrastructure costs. Considering the augmented total cost of ownership (per km), charging, network and auxiliary infrastructure costs contribute 10–19%. A gradual transition to battery electric buses over 12 years yields further TCO savings of between 4.6 and 5%. We recommend streamlining the network augmentation process to reduce uncertainty and project delays