Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Beyond the last mile: different spatial strategies to integrate on-demand services into public transport in a simplified city
No full textIntegrating on-demand services into public transport networks might be the best way to face the current situation in which these new technologies have increased congestion in most cities. When cooperating with on-demand services rather than competing with them, public transport would not risk losing users, and could attract some passengers from private modes thanks to an increased quality of service. This fact has engendered a growing literature discussing how to design such an integrated system. However, all of that research has imposed that on-demand mobility is to solve the so-called “last-mile problem”, serving only as a feeder that connects the exact origins/destinations with the traditional public transit network. As it induces a large number of transfers and it precludes some scale-effects to be triggered, in this paper we challenge that imposition and investigate if this is the best spatial integration strategy. To do so, we study a simplified linear city in a morning peak situation, where we propose seven different line structures, all of them combining a traditional fixed line with on-demand ride-pooling (ODRP): three direct structures, where ODRP can serve full trips, three semi-direct, where a single ODRP vehicle can serve the largest part of a trip, and a base case in which ODRP is restricted to the first and final legs only. Our results show that the base case is optimal only under very specific demand patterns, or when transfer penalties are disregarded. Our analytical approach reveals relevant operational aspects of such integrated systems: namely, that the base case can help increase directness (diminishing detours), and that ODRP can help shorten the routes of the fixed services to decrease operator costs
Contribution of built environment factors and their interactions with subway station ridership
No full textExploring the built environment\u27s impact on subway station ridership can aid in developing effective built environment update strategies. Previous studies lack an analysis of the impact of the interaction effects of built environment explanatory variables on subway station ridership. Beijing is divided into three zones with different buffer scales, and 18 built environment explanatory variables are selected as independent variables based on the ‘7D’ dimension of the built environment, and these are calculated based on a hypothetical circular scale range centered on a subway station. The inbound ridership of subway stations during morning peak hours, outbound ridership of subway stations during morning peak hours, inbound ridership of subway stations during evening peak hours, and outbound ridership of subway stations during evening peak hours are taken as dependent variables. For different dependent variables, the optimal parameters-based geographic detector (OPGD) model determines the recommended scale combination of the built environment around subway stations for three zones. Moreover, the impact of single explanatory variables and the interaction of explanatory variables on subway ridership were explored at the recommended scale combination based on the OPGD model. The results show that: (1) the recommended circular buffer radius combinations for the inbound ridership of subway stations during morning peak hours, outbound ridership of subway stations during morning peak hours, inbound ridership of subway stations during evening peak hours, and outbound ridership of subway stations during evening peak hours are 800–800–2000 m, 800–1000–2000 m, 800–1000–2000 m, and 800–800–2000 m, respectively. (2) The density of apartment facilities and the density of office facilities are the variables that contribute substantially to the ridership of Beijing subway stations. (3) The interaction between multiple explanatory variables has a much stronger contribution to ridership than single factors. In particular, the contribution of the explanatory variables to inbound ridership during the morning peak and outbound ridership during the evening peak increased significantly. Based on the analysis results, targeted built environment updating strategies are provided from the perspective of supply–demand balance, which can provide an important decision-making basis for updating the built environment around subway stations. In addition, it can also provide a theoretical basis for delineating the scope of transit-oriented development (TOD) in Beijing
Measurement and prediction of subway resilience under rainfall events: An environment perspective
No full textRainfall events frequently disrupt the subway system, significantly impacting operational efficiency and service quality. It is challenging to measure and predict subway system resilience due to the different construction environments of subway stations. We develop an approach based on probabilistic modeling techniques to measure subway system and station resilience. Random forest is used to analyze the heterogeneity of resilience patterns from an environmental perspective. Based on wavelet decomposition and spatial–temporal networks, we design an ensemble neural network modeling framework considering environmental factors to predict system and station resilience. According to an analysis of a dataset from Harbin, China, subway system resilience decreases by 1/6 for every 10 mm increase in rainfall intensity when the rainfall is under 60 mm. 44.6 % of low-resilience stations are near roads at the Level of Service III and IV. The proposed prediction model outperforms the state-of-the-art models with a prediction accuracy of 96.82 %
Partial Electrification Strategies for Diesel Commuter Rail’s Climate Challenge
No full textAs societal attitudes toward fossil fuels shifts, commuter railroads may be coming under increased scrutiny for their contribution to greenhouse gas (GHG) emissions. This analysis explores new possibilities created by battery-electric locomotives (BELs) in conjunction with partial electrification for en-route recharging in electrified territory. We propose a systemwide network approach that starts with one or more substations in geographically strategic locations, then electrifying just enough for sufficient electrical charge, with BELs running off the wire in non-electrified areas. As 25,000-V alternating-current substations generally have an 18–26-mi reach, considerable possibilities exist for new-start electrifications. This is significantly more cost effective than a traditional approach that electrifies one corridor at a time. Although BELs are in technical development, and certain implementation challenges remains on commuter railroads, we believe BELs required to enable this type of electrification are within reach of current battery technology. Drawing on examples in Boston, Philadelphia, Chicago, and Minneapolis, six strategies are outlined: (1) minimizing electrification costs by electrifying radial commuter networks from a centrally located substation, (2) for systems with longer routes, using BELs to extend the central substation’s reach, (3) extending new electric service beyond existing electrifications with BELs, (4) using BELs to create new trans-regional services, (5) co-locating railroad-owned feeder lines with utility infrastructure such as electric transmission rights-of-way to maximize the geographic reach of supply substations, and (6) providing charging pads in certain limited situations. Preliminary ridership, energy sufficiency, and lifecycle cost analyses were performed to show the feasibility of BEL technology in conjunction with a substation-based, supply-side approach to designing electrification projects
TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management
No full textTransit users’ lifecycle behavior pattern transition reflects the continuous and multi-phase changes in how frequently and regularly users utilize public transit over their lifetime. Predicting transit users’ lifecycle behavior pattern transition is vital for enhancing the efficiency and responsiveness of transportation systems. Thus, this study incorporates lifecycle analysis in predicting long-term sequential behavioral pattern transition processes to go beyond just examining user churning at a single point in time. Specifically, this study proposes the TripChain2RecDeepSurv, a novel model that pioneers the individual-level analysis of lifecycle behavior status transitions (LBST) within public transit systems. The TripChain2RecDeepSurv is composed of (1) the TripChain2Vec module for encoding transit users’ trip chains; (2) the self-attention Transformer module for exploring the latent features related to spatiotemporal patterns; (3) the recurrent deep survival analysis module for predicting LBSTs. We demonstrate TripChain2RecDeepSurv’s predictive performance for empirical analysis by employing Shenzhen Bus data. Our model achieves a 74.39% accuracy rate in churn determination and over 80% accuracy in status sequence identification on the churn path. In addition, our findings highlight the segmented nature of Kaplan-Meier curves and identify the optimal intervention time against the user churning process. Meanwhile, the proposed model provides individual-level heterogeneity analysis, which emphasizes the significance of customizing user engagement strategies, advocating for interventions that extend users’ engagement in patterns with high-frequency transit usage to curb the transition to less frequent travel usage
Causality between multi-scale built environment and rail transit ridership in Beijing and Tokyo
No full textRecent years have witnessed researchers\u27 academic interests in the relationship between the built environment and rail transit ridership. However, few studies have analyzed it from a beyond-station scale and causal perspective. To fill this gap, this study uses Beijing and Tokyo as cases and utilizes Bayesian networks and generalized propensity score matching to achieve causal discovery and causal inference of built environment and ridership. Moreover, we innovatively consider the line-scale built environment factors. Evidence suggests that transit agencies should promote the overall accessibility of stations along the line to employment concentration areas more than single-station accessibility in Beijing. Meanwhile, the positive impact of bus-rail transit line cooperation on ridership is limited in Beijing. Planners should consider the capacity of rail transit lines. The causal inference results are mainly in line with previous correlation studies, but some interesting differences exist. For example, the net effect of dense pedestrian roads on ridership is negative, reminding policymakers that they should avoid expanding pedestrian paths without developing ridership-attracting resources. This study uses causal theories to emphasize the necessity of corridor-based and modest development
Disproportionate impact of weekday-to-weekend transit service cuts on access for disadvantaged populations
No full textThis study examines uncertainty in access and access loss by measuring the relative and absolute differences in the number of reachable opportunities when catching public transit with the shortest and the longest wait times. An evaluation of uncertainty in access and access loss on both weekdays and weekends in the Washington Metropolitan Area yields three findings. First, the weekday-to-weekend service reduction disproportionately impacts Black, Millennial, low-income, and carless households. Second, the U.S. capital bears less uncertainty in access but more access loss than its neighboring counties. It is noticed that a quarter of the population resides in areas with high access uncertainty and high access loss; Asians and carless households, respectively, comprise the highest and the lowest shares. Third, there is a negative correlation between uncertainty in access and transit ridership. The findings also echo that transport equity should be approached through an intersectional lens as vulnerable groups often overlap
Cost analysis of different vehicle technologies for semi-flexible transit operations
No full textIn low-demand areas, semi-flexible transit system (SFT) operated by battery electric vehicles (BEVs) can reduce operational costs and achieve zero emissions, allowing SFT to be used more widely, and transit agencies to benefit more significantly. This paper is aimed at analyzing the effect of the additional requirements of BEVs on the cost efficiency of SFT services while considering different headways and slack time to accommodate route-deviation. Analytical models are used for detailed estimation of the total cost, including operator, user, and environmental costs, allowing a comparison with internal combustion engine (ICEV) vehicle technology, and three vehicle sizes: minivans, standard vans, and minibuses. Study results can be used to evaluate budget requirements to upgrade an existing ICEV based standard bus service along an underperforming low demand route to a BEV based SFT service. The application of the proposed methodology is demonstrated for a low-demand bus route in Regina, Canada
A novel ranking method based on semi-SPO for battery swapping allocation optimization in a hybrid electric transit system
No full textThe allocation of batteries in hybrid charging stations has consistently played a significant role in the decision-making process for plug-in charging and battery swapping. Predicting the state of charge (SOC) for each electric bus (EB) in advance is crucial to assist in making future battery allocation decisions. This paper proposes a semi-Smart “Predict, then Optimize” (semi-SPO) framework for the battery allocation scheduling problem. The battery allocation optimization problem is reduced to a ranking problem with respect to SOC and integrated into the prediction model. The rank of SOC is determined from both pairwise and listwise perspectives. Considering the inherent characteristics of rankwise regression, such as missing parameters and infinite number of optimal solutions, a geometric analysis is conducted. A model enhancement approach is then proposed to ensure the accuracy of both prediction and optimization models. This enhancement facilitates optimal decision-making while preserving the interpretability of predicted values. A case study is conducted using the real-world data of Nanjing, China. The result shows that the proposed semi-SPO framework offers superior decision-making outcomes in the battery allocation pro
Methods of measuring mode share and mode shift at different spatial scales and timescales
No full textThe purpose of this research project was to investigate current methods to measure mode share and mode shift in person-kilometres travelled (PKT) at subnational level (regionally and locally), for different spatial scales and timescales, and for three transport modes: active (walking and cycling), private and public.
The New Zealand Household Travel Survey (HTS) and New Zealand Census of Population and Dwellings (the Census) are the primary means to monitor travel behaviour in New Zealand. The HTS is conducted annually but has a small sample size. This means that data must be aggregated over several years to produce meaningful results, or even longer to get sufficient samples outside major urban areas. The Census has a large sample size but is conducted only once every five years. Unlike the HTS, it captures only simplistic information about travel activity.
This project involved consulting with stakeholders to identify and review current and emerging technologies and approaches for measuring mode share in New Zealand and overseas. The approaches were categorised as national or regional (household travel surveys), key corridors and city centres (screenline approaches) and transport networks or regions (link-based approaches). The project identified that several authorities (this includes regional and local councils, and transport authorities) are making significant progress towards continuously monitoring transport activity, but they are currently at different stages of upgrading their data-collection approaches and infrastructure. The project identified that consistent guidance on measuring and reporting mode share and mode shift is needed.
A pilot study was conducted that involved transport data provided by four authorities. The data was used to develop a proof-of-concept dashboard that demonstrates it is feasible to develop a framework for measuring mode share and mode shift consistently.
The research culminated in a toolkit for measuring mode share and mode shift. It offers guidance on collecting, processing, analysing and reporting data. As authorities continue to improve their data infrastructure, the toolkit will help generate consistent mode-share and mode-shift results that can be used for numerous applications. These include project appraisals, before-and-after evaluations, monitoring of long-term trends, and sustainability and resilience impact assessments.