Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Random Regret Minimization Approach to Commuting Mode Choice in São Paulo, Brazil
No full textThe random regret minimization (RRM) approach has been widely used in transport literature, but its application in the Global South is still marginal. In this paper we discuss individual commuting mode choice in the city of São Paulo (Brazil) from the perspective of the RRM modeling approach and its variants found in the literature. We estimated several multinomial logit models (random utility maximization [RUM], classical RRM, uRRM, and hybrid formulations of RUM-RRM models) and explored regret scale and decision rule heterogeneities using latent class models with specific u parameters. The results showed that the RRM approach outperformed its RUM counterpart in relation to model fit and suggested that it better captured the mode choice behavior of individuals in the analyzed context. We also found that accounting for heterogeneity in scale and decision rules improved the results of the models, and the specific u parameters indicated that individuals displayed different regret behavior for travel time and travel cost attributes
Trends in Toronto’s Subway Ridership Recovery: An Exploratory Analysis of Wi-Fi Records
No full textThe COVID-19 pandemic has left major shifts in transit usage patterns on systems around the world in its aftermath. Unfortunately, the lack of detailed post-pandemic data on passenger travel habits has limited transit agencies’ ability to respond to trends and leverage new travel markets. The rollout of wireless fidelity (Wi-Fi) services at stations and onboard vehicles presents a potential solution, as Wi-Fi device connections can be used to provide very detailed information on customers’ origins, destinations, exact route, and travel time, which in turn can be aggregated by time and geography to reveal broader trends. This study presents an exploratory analysis based on such Wi-Fi data to investigate post-COVID ridership recovery trends on the Toronto subway system, demonstrating that Wi-Fi connections can be a credible proxy for overall ridership. The data show that downtown office commuting has been the slowest-recovering travel market, with local riders in suburban areas, off-peak riders, and discretionary riders returning to the subway system at higher rates. The data also confirm past research findings that less affluent and non-office workers were the fastest to return to transit
Deep Learning Model for Short-Term Origin–Destination Distribution Prediction in Urban Rail Transit Network Considering Destination Choice Behavior
No full textUrban rail transit (URT) has emerged as a crucial mode of transportation in metropolitan areas. For the effective operation of expanding URT networks, accurate short-term origin–destination (OD) demand distribution predictions are essential. This study introduces a novel deep-learning-based model for predicting short-term OD distribution in extensive networks, taking destination choice behaviors into account. First, we perform a comprehensive analysis of station passenger flows and OD flows from both temporal and spatial dimensions. Then, we develop the origin–destination distribution prediction (ODDP) model, combining the destination choice model (DCM) with the deep learning model (DLM). The DCM aims to understand OD distribution patterns from a behavioral perspective by transforming real-time inflows into OD distributions. Meanwhile, the DLM, employing attention and convolution layers, effectively captures the intricate temporal and spatial dynamics of passenger flows. Our model is evaluated using data from the Guangzhou Metro network in China, showing significant enhancements in prediction accuracy, model interpretability, and overall robustness. The implementation of our model promises substantial benefits for the operational efficiency of URT systems
Designing an Autonomous Mobility-on-Demand Service for Transit Last-Mile Access
No full textPublic transportation has the potential to provide a safe, convenient, affordable, and eco-friendly mobility service. However, because of its fixed routes and limited network coverage, it is sometimes difficult or impossible for passengers to walk from a transit stop to their destination. This inaccessibility problem is also known as the “transit last-mile connectivity problem.” Such a lack of connectivity can force travelers to drive, consequently increasing vehicle ownership and vehicle miles traveled (VMT) on the roads. The autonomous mobility-on-demand (AMoD) service, with characteristics such as quick fleet repositioning and demand responsiveness, has the potential to provide last-mile coverage where fixed-route transit can only provide limited coverage. This study aims to address the last-mile problem by developing an online demand-responsive AMoD service integrated with fixed-route transit. A linear time-delay dynamical system is proposed to model the AMoD system, and a model predictive control methodology is adopted to regulate the system around an equilibrium point with minimum vehicle rebalancing. To assess the impact of this new mobility service on travel demand, a simulation study is developed and integrated with a mode choice model capturing a combined transit-AMoD model. The experiment results reveal the potential to enhance transit efficiency, reduce VMT, and accommodate the increased transit demand while maintaining the quality of service
Energy-Efficient Train Operation Optimization in Istanbul Metro Network Considering Variable Passenger Numbers
No full textThis manuscript presents a speed trajectory optimization to minimize driving energy consumption using four different nature-inspired algorithms. For this purpose, a model was developed that can simulate motion of a single train. To test the train motion model and speed trajectories, Istanbul M3 subway line was modeled with its actual parameters such as gradient, curve, and speed limit. Concerning computational time and energy usage, a straightforward speed profile and proposed speed profiles by the algorithms have been compared. Afterwards, variable passenger numbers at each station in a certain hour were taken as dynamic mass input. With the marine predator algorithm (MPA) that finds the best result among the four, speed profile optimization was performed again under the effect of unstable mass. Finally, a train was operated by a driver according to the optimal speed profile. As a result, energy saving is 18.75% in simulation environment and 21.27% in real-life test
Variation in Bus Transit Attribute Perceptions between Australian Cities
No full textThis paper explores user/nonuser perceptions of bus transit attributes in Melbourne, Sydney, and South East Queensland (SEQ). It aims to understand the relative importance and performance of various bus transit attributes and how the assessment varies according to city, socioeconomic cohort, and user/nonuser groups. Primary data were collected via a screening survey, enabling a very large sample (n = 13,537) and a smaller, more representative subset collating more detailed survey data (n = 2,420). Personal safety stood out as a key bus transit attribute of importance in all cities. Specifically, safety when traveling on the bus during the daytime and at night, and safety getting to and from the bus stop were ranked as very important bus transit attributes. Some findings differed by region: service levels, punctuality, frequency, and timetable adherence (reliability) were rated relatively high in importance but low in performance in Sydney and Melbourne compared with SEQ. This suggests that Melbourne and Sydney might require service-level improvements more immediately than SEQ. Implications for policy for each Australian metropolitan region (Sydney, Melbourne, and SEQ) are later discussed
Ultrafine Particles on Electric, Gas, and Diesel Buses in the Mass Transit Buses of Bogotá, Colombia
No full textThe concentration of traffic-related air pollutants (TRAPs) inside transport microenvironments takes on relevance today in many megacities with high population density, intense traffic, and prolonged travel times. These conditions can intensify exposure to TRAPs and worsen public health problems. TRAP concentrations in these microenvironments are changing because of the introduction of cleaner technologies. In this study, we compare the ultrafine particles measured inside diesel, gas, and electric buses during their normal operation in Bogotá, Colombia. We used a miniature diffusion size classifier (DiSCmini) to measure ultrafine particle concentrations, average particle size, and the lung-deposited surface area. Our results revealed significantly lower levels of this pollutant inside electric buses. The concentration of ultrafine particles per cubic centimeter is about 41% and 27% lower in electric buses compared to diesel and gas, respectively. The lung-deposited surface area is also lower in electric buses. Nevertheless, the average particle size in electric buses is 10% and 18% smaller compared to diesel and gas, respectively
Impacts of transit-oriented development on car use over a 10-year period in Porto, Portugal: From macro- to micro-analysis
No full textTransit-oriented development (TOD), an urban planning concept that aims to promote sustainable transport modes, has been actively studied in recent years, especially in relation to car trips reduction. However, longitudinal studies on the matter are still rare. In this paper, we analyze the effects of the implementation of a new metro system after its first 10 years of operation focusing on how changes in the number of car trips were influenced by station type – TOD, transit-adjacent development (TAD) and park-and-ride (P&R). Specifically, we perform a before/after analysis of the impact of metro implementation in the Porto area (Portugal) both at a macro scale (civil parish) and at a micro scale (census tract) to analyze the overall effect of metro and more detailed effects only detectable at the micro scale. Census-tract data enables a comprehensive analysis of spatial spillover impacts from different stations, comparing the extent (i.e., the distance range within which the effect of station proximity is noticeable) and the magnitude (the reduction in the number of car trips) of the spillover in each case. Both direct and indirect metro impacts are already visible at the macro scale, yet at the micro scale the magnitude of the spillover effects varies depending on station type. The effects of TOD stations on the reduction of car trips are the strongest across the different station types and are felt up to a 2 km distance from a station, while TAD and P&R effects are weaker and do not reach beyond 1.2 km
Using different transport modes: An opportunity to reduce UK passenger transport emissions?
No full textIn 2022, transport accounted for around one third of UK territorial emissions. Many decarbonisation pathways include modal shift as a possible way of reducing passenger transport emissions. Yet, the mitigation potential of modal shift often depends on generic behavioural assumptions rather than the technical feasibility for using different transport modes. Here travel microdata from the UK is used to systematically assess which trips could use more efficient transport modes, establishing a theoretical maximum for how much modal shift could take place with no reduction in mobility. Compared to current passenger transport use, emissions could be reduced by about 30% by changing the transport modes used for personal travel. This is possible if car use is reduced by approximately 27% and mainly replaced by trains. Further emissions savings are possible by increasing bicycle and motorcycle availability, increasing capacity of coach and surface rail, and increasing the time typically spent travelling
Investigation of the interaction between urban rail ridership and network topology characteristics using temporal lagged and reciprocal effects: A case study of Chengdu, China
No full textThis study investigates the interaction between the network topology characteristics of the urban rail transit (URT) system in Chengdu, China, and the ridership at the station level between 2013 and 2019. In particular, the relationship between a station’s importance, measured in terms of its ordered betweenness centrality (OBC), and its ridership pressure, measured as the number of high ridership days (HRD) a year, is assessed. Seven covariates for socioeconomic attributes, the availability of other modes, land-use conditions, and number of entrances and exits of rail stations are also included. Four hypotheses are proposed to investigate the temporal lagged and reciprocal effects of OBC and HRD on each other. To assess the temporal lagged effects, the study period is divided into two waves: Wave 1 from 2013 to 2016 and Wave 2 from 2016 to 2019. Four generalized structural equation models are combined with cross-lagged panel models to explore the hypothesized causal relationships. The results indicate that a rail station that experiences high ridership is less likely to be topologically important in later years. In contrast, a station with a higher importance ranking is more likely to experience high ridership pressure in later years; however, this relationship is weakened if the reciprocal effect of ridership pressure on the station’s importance is considered. In addition, the causality of the temporal and reciprocal effects indicates that the URT development strategy may affect the interaction between OBC and HRD. The effects of the covariates on OBC and HRD differ between Wave 1 and Wave 2, suggesting that the influence of these covariates varies with the URT development stage