Monash University, Institute of Transport Studies: World Transit Research (WTR)
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How individual perceptions of transportation systems influence mode choice for mobility-challenged people: A case study in Dhaka using an integrated choice and latent variable model
No full textThis study examines how mobility-challenged persons (MCPs) navigate the limitations of transportation systems, using an integrated choice and latent variable (ICLV) model. Building on the experience of MCPs in a South Asian megacity context, that of Dhaka, Bangladesh, the empirical strategy accounts for perceptions of mode-specific challenges, which arguably vary by mobility impairment. These perceptions are posited as latent variables. Drawing on survey data collected from 400 MCPs living in Dhaka, the latent variables were constructed via a factor analysis of 18 statements about the experienced severity (ranked on a scale from 1 to 5) of mode-specific challenges. Holding socio-demographic and travel-related factors constant, we find that perceptions of mode-specific challenges significantly influence mode choices – while the degree of impairment alone, and related mobility aid needed, do not. Perceived limitations of the walking infrastructure shift MCPs\u27 travel demand towards the bus, whereas bus fare-related issues encourage the use of non-motorized and powered three-wheelers. We recommend that mode choice models include latent variables related to MCPs’ perceptions of various modes to more accurately inform universal access policies
Powering up urban mobility: A comparative study of energy efficiency in electric and diesel buses across various lane configurations
No full textThis paper undertakes a comprehensive quantitative and qualitative analysis of the energy consumption characteristics of electric buses (EBs) and diesel buses (DBs) on different lane configurations (including regular, dedicated, and shared-use bus lanes) and different operational conditions. We resort to both data- and simulation-based approaches to investigate this topic. For this, we use GPS trajectory data from four transit corridors in Xi’an, China, which is complemented by a simulation-based analysis using a microscopic traffic simulator to mimic traffic dynamics on a 1-kilometer ring road. We show that EBs effectively consume less energy in suburban areas when utilizing regular lanes, while a dedicated bus lane layout offers minimal to no energy consumption benefits for DBs. Conversely, substantial energy savings are anticipated for both EBs and DBs when operating on dedicated bus lanes in downtown areas. Our simulation-based analysis showcases that the energy consumption of both bus types increases by over 25% on regular lanes compared to dedicated bus lanes under congested traffic conditions. Notably, shared-use bus lanes consistently exhibit the highest energy consumption in most scenarios, therefore being the less environmentally friendly lanes for deploying EBs and DBs
Pedestrian movement with large-scale GPS records and transit-oriented development attributes
No full textTransit-oriented development (TOD) is a widely accepted strategy for sustainable urban planning that encourages walking and transit ridership. Walking behavior in metro station areas is often quantified using pedestrian volumes. However, even in areas with similar pedestrian volumes, the walking distance and time spent within metro station areas may vary. Thus, a more comprehensive approach is required to evaluate pedestrian movement in a TOD context. Accordingly, we developed pedestrian movement indices (PMIs), which aggregate individual pedestrian movement as an area characteristic to capture pedestrian count, distance walked, and time spent based on large-scale GPS data. Furthermore, we illustrated the differences in the relationship between PMIs and TOD attributes according to each index using density, diversity, design, destination accessibility, and distance to transit (or the “5Ds”). A comparison of the three PMIs showed that pedestrian count, distance, and time spent provided different information for evaluating pedestrian movement. TOD attributes’ impact around metro stations varied, depending on the PMIs. These results indicate that policymakers should select appropriate measures to meet their policy aims when assessing pedestrian movement within metro station areas. This study contributes to monitoring pedestrian movement and incorporates pedestrian movement into spatial planning efforts considering TOD
TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies
No full textThe impacts of transit-oriented development (TOD) on travel behavior have been extensively studied, with a predominant focus on cross-sectional analyses that provide a static evaluation at a specific point in time by comparing TODs and non-TODs. Longitudinal assessments that capture changes in behavior over time remain relatively uncommon, and the literature tends to overlook differences in evaluating TOD effects across cross-sectional and longitudinal analyses. Additionally, the role of trip purpose as a significant but unexplored variable influencing the degree of TOD effects is often disregarded. To address these gaps, this systematic review examines 48 quantitative studies, comparing the effects of TOD on travel behavior from cross-sectional and longitudinal perspectives, restructuring indicators of effects into transit use, non-motorized travel, vehicle dependence, and vehicle ownership, and differentiating the effects by trip purpose. A metric has been introduced to quantitatively assess the impact of TOD on travel behavior. The pooled results indicate that private vehicle usage remains high in TOD areas, particularly for non-commuting trips, and that the longitudinal effects of TOD are limited and potentially influenced by individual travel attitudes, residential self-selection, and long-term travel habit change. Furthermore, the methodological differences between cross-sectional and longitudinal studies may lead to divergent conclusions regarding the effects of TOD on travel behavior. Our analysis sheds light on the importance of carefully selecting an appropriate method for a given research question to maximize the accuracy and relevance of the findings. Combining TOD and shared mobility can create a more efficient multi-model transport network that meets the diverse needs of city residents and improves accessibility for all. Overall, this review provides new insights into the impacts of TOD on travel behavior and supports the potential for a paradigm shift toward multimodal transport through the integration of TOD and shared mobility
The Road Less Traveled: Does Rail Transit Matter?
No full textQuantifying the effect of rail transit on vehicular traffic helps policy makers understand its transportation benefits. Previous studies seldom consider the effect over time and the influence of confounding factors. We apply a quasi-experiment research design to explore the evolving impact of the Green Line light rail transit on vehicular traffic in the Twin Cities, controlling for road classification, land use, and transit supply. The results show that rail transit is a substitute for automobile traffic, but induced and diverted trips gradually reduce the substitution effect. The reduced effect suggests that rail transit improves transportation system performance
The Multimodal Accessibility Target (MAT)
No full textIf an infrastructure intervention, such as the conversion of a car lane into a dedicated bus lane, decreases automobile accessibility but increases transit accessibility, what is the overall effect on accessibility? To date, no methods have been proposed to evaluate this question. Accessibility research in the transportation and land-use literature has been dominated by unimodal and comparative approaches to analyzing accessibility. Little attention has been paid to quantifying multimodal accessibility or to the interactions between modes, and how these might affect overall accessibility. Moreover, the use of targets for accessibility in planning has been largely ignored. Particularly important to the evaluation of transportation planning outcomes is accessibility to employment, as this is a key element and predictor of urban economic prosperity. This study, building on the work of Alain Bertaud, proposed the multimodal accessibility target or MAT. The MAT provides a more accurate picture of overall (across mode) accessibility to jobs in a city, and can be used to evaluate transportation infrastructure investments. It also provides a target for accessibility that is an easily interpretable indicator that can serve as a goal in accessibility-based transportation planning. A case study of the proposed implementation of a bus rapid transit (BRT) system in Montreal, Canada was used as an empirical example of the use of the MAT. In the case study, predicted multimodal accessibility to employment in the study area (and thereby the MAT) was found to increase with BRT compared with the base case scenario
Customised bus route design with passenger-to-station assignment optimisation
No full textAs an emerging and innovative public transportation mode, the customised bus (CB) has drawn widespread attention. Existing studies of the CB mainly focus on issues regarding the bus station location, route design, timetabling, and fare setting, but rarely consider the passenger to bus station assignment problem which can significantly influence passengers’ benefit and the buses’ operating scheme. Thus, this study focuses on the customised bus routing problem with passenger-to-station assignment (CBRP-PSA) aiming to simultaneously minimise passengers’ CB service access cost and the bus route cost. A time-discretized multi-commodity network flow model is developed to jointly determine the CB routes, timetables, and passenger-to-station assignment by allowing split loads and mixed loads. Through the dualization, the developed model is decomposed into two solvable sub-problems, and a Lagrangian-based heuristic solution algorithm is proposed. The model and algorithm are implemented in illustrative, medium-scale, and large-scale transportation networks to demonstrate their effectiveness under different scenarios
Inferring alighting bus stops from smart card data combined with cellular signaling data
No full textAlighting bus stops inferring is of great significance for origin–destination estimation. Cellular signaling data (CSD), a kind of individual trajectory generated by mobile phones, provides a new idea for alighting stop identification. To explore the capacity of CSD in this field, this study proposes a method of inferring alighting bus stops by integrating smart card data, bus GPS data, and CSD. Firstly, a correspondence table is generated by individual matching, which correspondingly links mobile phone users in CSD and bus passengers in smart card data. Secondly, the inferred alighting bus stops are determined by the radii of the circumscribed circles of triangles consisting of directly projective points, piecewise projective points, and CSD points. The proposed method is verified by an experimental dataset from a behavioral simulation experiment of 10 volunteers in Foshan, China. The results show that the recognition rate is 92.94% and the inference accuracy is 65.82%, or 93.67% under a one-stop error. In the case of a real dataset in Foshan, the proposed method with a recognition rate of 53.02% highly outperforms the trip-chain-based method. The difference in the recognition rate between the two datasets is due to that the real dataset is more likely to be incomplete than the experimental data, which indicates that the performance and effectiveness of the proposed method are sensitive to the data quality and completeness of CSD and bus GPS data. Having said that, the proposed method can infer both alighting stops of linked bus trips and single unlinked bus trips
Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach
No full textThe transfer city is a key point in air-to-rail intermodal travel (ARIT) that directly influences the service level of the entire system. Although some studies have investigated factors that influence passengers’ ARIT preferences based on subjective surveys, an in-depth understanding of their nonlinear and interactive impacts on passengers’ actual behavior is still lacking. Using passengers’ online booking data in China, this study implements an interpretable ensemble machine learning framework that incorporates decision-making theory to unveil feature importance and the complex nonlinear and interactive effects of various attributes on passengers’ choice of transfer city in the crucial ARIT scenario. The results show that (1) the extreme gradient boosting (XGBoost) model achieves better performance in predicting ARIT transfer city choice than the conventional discrete choice model; (2) attributes related to intermodal services (e.g., ticket price, in-vehicle duration, transfer duration, quantities of flights and trains) are more important than personal demographic characteristics (e.g., age and gender); (3) factors related to service economy and efficiency display nonlinear impacts with fluctuations and critical thresholds; and (4) individuals with different characteristics present heterogeneous preferences for ARIT transfer cities. These findings can provide useful managerial implications for policymakers