Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Is customized bus service for commuter segments the need of the hour? An integrated IPA-machine learning framework to redefine commuter segments based on quality expectations
No full textBus service in emerging countries faces significant challenges such as inferior quality, limited appeal, and declining ridership, which is further aggravated with increased affordability of commuters. The conventional approach of providing a standardized service to all commuters has proven ineffective due to the diverse perception and requirements of commuters. Unlike traditional approaches that categorize commuters solely based on car ownership, the present study employs a comprehensive approach to segment commuters based on their service quality expectations and identifies improvement areas specific to each segment. The methodology uses integrated Importance Performance Analysis-Machine learning (IPA-ML) based framework to identify segments and improvement areas. While demonstrating the methodology in an Indian metro city, two segments, namely ‘tolerant users (low quality acceptors)’ and ‘quality seekers (high quality seekers)’, are identified using complete-linkage-hierarchical clustering with Naïve Bayes Machine Learning classifier. Intervention areas specific to these segments are identified using revised-IPA with Kernel Initialization technique giving due considerations to the segment wise factor structure and management schemes. The findings indicated the impact of multiple socio-economic factors on segment formation emphasizing the need for a holistic approach beyond car ownership when segmenting commuters. The significant differences in perceived improvement areas between the two segments highlight their distinct perceptions and expectations, indicating the necessity for segmenting the bus service to cater to their specific needs. While study provides case-specific findings for the improvement of bus service, the methodology and experience give a new direction to develop bus as a sustainable demand management instrument in a broader context
How do German cities translate global sustainability visions into local mobility planning? A quantitative analysis of planners\u27 perspectives and priorities
No full textSustainable development has been the guiding political principle worldwide since the adoption of the UN SDGs in 2015. Transport is of highest relevance for meeting this ambition; it enables people to move to destinations to meet their needs, and it causes substantial negative effects in the social, economic, and environmental dimensions. Cities are of highest relevance for transport because of their prevalence, and because challenges and opportunities are particularly pronounced in cities, but few attempts have been made to evaluate how cities translate the higher-level sustainability ambitions to their local contexts. Based on a survey of 402 municipalities in Germany, this study investigates local stakeholders\u27 priorities in terms of sustainable urban mobility (SUM). Expert planners in local administrations assign higher priority to all SUM aspects than this is the case in the official local strategic planning objectives, such as those formulated in Sustainable Urban Mobility Plans (SUMPs), with congestion being the only exception. Accessibility and further domain-specific aspects consistently get higher priority than the environmental effects of transport. Local stakeholders consistently commit first and foremost to the function of transport systems and give the minimization of negative effects only secondary priority. Priorities assigned to the SUM aspects are higher in larger cities than in smaller cities, particularly for the expert planners\u27 assessments. Further studies with similar designs in other parts of the world would help to better understand the transferability of the mechanisms identified in this study and support higher-level efforts to achieve sustainability goals
City-forming role of the Metro in Warsaw
No full textThe article analyses the continuous transformations in the spatial development of the western part of Warsaw from 2001 to 2022. The study utilises cartographic materials and temporal analysis, considering pedestrian travel times to planned metro stations in the Bemowo district. An investment analysis of single and multi-family buildings was conducted throughout the Bemowo district and near metro stations. The analysis is based on four time intervals to determine the speed and nature of changes and their impact on land development. The selected analysis periods are 2001–2005, 2005–2010, 2010–2016, and 2016–2022. In 2022, the construction of the last section of the second metro line commenced. The results indicate that the total number of buildings constructed in Bemowo within 833 m of the metro station increased by 298 between 2001 and 2022, representing a growth of 64.5 %. During the same period, the building area increased by 1,237,405 m2, corresponding to a growth of 136.2 % relative to the existing area. The highest increase in the number of new multi-family buildings was observed after 2016, providing evidence of the alignment of the construction and the second metro line station with the transit-oriented development (TOD) concept in the western part of Warsaw
Unsupervised origin-destination flow estimation for analyzing COVID-19 impact on public transport mobility
No full textThe outbreak of COVID-19 caused unprecedented disruptions to public transport services. As such, this paper proposes a methodology for analyzing COVID-19 impact on public transport mobility. The proposed methodology includes: (1) a new unsupervised machine learning (UML) method, which utilizes a decoder-encoder architecture and a flow property-based learning objective function, to estimate the origin-destination (OD) flows of public transport systems from boarding-alighting data; and (2) a temporal-spatial analysis method to analyze OD flow change before and during COVID-19 to unveil its impact on mobility across time and space. The validation of the UML method showed that it achieved a coefficient of determination of 0.836 when estimating OD flows using boarding-alighting data. Upon the successful validation, the proposed methodology was implemented to analyze the impact of COVID-19 on the mobility of the New York City subway system. The implementation results indicate that (1) the rise in the number of weekly new COVID-19 cases intensified the impact on the public transport mobility, but not as strongly as public health interventions; and (2) the inflows to and outflows from the center of the city were more sensitive to the impact of COVID-19
Characterising travel behaviour patterns of transport hub station area users using mobile phone data
No full textUnderstanding the travel behaviour of transport hub users is vital for improving transport services. Although previous research has explored passenger behaviour in and around transport hubs, there is a lack of comprehensive studies on travel patterns within hub station areas. To this end, this study harnessed mobile phone data to analyse the travel behaviour within a hub station area, using Beijing South Railway Station as a representative case. A series of recognition criteria was set up based on spatio-temporal information to categorize users within the station area. We categorised station area users into transport hub users (THU) and non-transport-hub users (NTHU) and examined their distinct travel patterns. The results reveal distinct travel patterns for THU and NTHU. NTHU are predominantly concentrated in and around the station area. THU, however, display longer trip distances and more widely distributed endpoints. In terms of travel time distribution, NTHU show evident morning and evening peak phenomena, while the travel time distribution of THU fluctuates throughout the day. The study also employed association rule analysis to illustrate strong connections between the station area, Beijing\u27s core, central areas, and even urban fringe areas. The results reveal that the station area is most closely connected to the core and central areas of Beijing, from where many users are attracted to the station area. In addition, some urban fringe areas also have strong connections with the station area. These findings inform urban transport planning and personalised travel services
Synergizing cycling and transit: Strategic placement of cycling infrastructure to enhance job accessibility
No full textEnabling cycling at the home side or at the activity side of transit trips has been recognized as a promising solution to address transit network discrepancies and enhance connectivity between residents and employment opportunities. However, this multimodal solution is conditional to bicycle parking and cycle lanes, and urban planners need tools to identify relevant locations for these infrastructures. This research presents a novel method to quantify the impact of potential cycling infrastructures on job accessibility. Using a logsum-based indicator, we assess the spatial distribution of accessibility improvements across neighborhoods when residents have the option to cycle from and to transit stops. Then, we quantify the individual contribution of every potential bicycle parking location and cycle lane to the overall accessibility improvements. The proposed approach offers valuable support to urban planners in identifying the best locations for (1) multimodal bicycle parking and (2) cycle lanes to foster synergies between cycling and transit. To demonstrate the efficacy of our method, we apply it to the case study of Amsterdam. The findings reveal that bicycle parking at metro stops and cycle lanes connecting these stops to dense and remote locations contribute the most to accessibility improvements, as they effectively connect these areas to high-frequency and high-speed transit lines. Additionally, we observe that few strategic infrastructures account for most of the accessibility improvements in Amsterdam
Land price dynamics in response to high-speed rail network characteristics: An empirical analysis
No full textHigh-speed railway (HSR) has become the backbone of China\u27s rapid and comprehensive urban transportation network system. However, the impact of the network characteristics of HSR on urban land prices has not been studied in sufficient detail. To fill this gap, this study focuses on both the Yangtze River Delta urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration and explores the linkage relationship between the HSR network and urban land prices. For this analysis, social network analysis and fixed effects model are used. The results show that the overall HSR network density of these two urban agglomerations still has room for improvement. Affected by regional proximity and the direction of HSR lines, HSR presents a spatial pattern of a multi-center–multi-tier network. The “siphon effect” is the main effect of the HSR network characteristics on land prices. However, the weakening degree of spatial spillover effect on siphon effect of the HSR network in the Yangtze River Delta urban agglomeration is stronger than that in the Beijing-Tianjin-Hebei urban agglomeration. Local governments should fully utilize the spatial effects of HSR and enhance the level of coordinated development among urban agglomerations
Joint Model of Transit Usage Frequency and In-Vehicle Safety Perception During the COVID-19 Pandemic
No full textSocial distancing strategies and strict hygiene adherence during the pandemic have added an extra dimension to the safety requirements of transit usage. Thus, travelers’ altered safety perceptions, which can affect transit usage, need to be assessed for effective policy decisions for the post-pandemic period. This study examined the interaction between in-vehicle safety perception and transit usage using an integrated approach by jointly modeling them, considering the fear of virus infection. A multivariate ordered probit model was developed for the investigation using a dataset collected through a web-based travel survey conducted in the Greater Toronto Area, Canada. The results reveal that, along with socioeconomic attributes, many pandemic-related variables and latent attitudinal factors affect the propensity to use transit. It is observed that those having a better safety perception of the bus are more inclined to use transit more frequently than others. Apart from safety perception, those who were more cautious, over the age of 34, and shifted to working from home during the pandemic had an adverse propensity to use transit. However, a higher propensity toward transit usage was observed for pre-pandemic transit users and for those who had a higher level of satisfaction with transit attributes during the pandemic. A similar tendency was also observed for fully vaccinated residents
Incident Delay Prediction in Urban Railway Systems: Methodology Review and Exploratory Comparative Analysis
No full textThe occurrence of incidents seriously affects the operation of the whole urban railway system and passengers’ travel experience. Accurate delay prediction is important for traffic control and management under incidents. Few studies were reported on incident prediction in urban railway systems because of the unexpected nature of incidents and the lack of comprehensive incident data. Existing models used to predict incident delay can be divided into statistical methods and traditional machine learning methods, as well as ensemble learning methods. This study conducts a methodology review for these models by comparing their performance in predicting incident delays using a large-scale incident dataset collected from an urban railway system in Hong Kong. Three statistical models and six machine/ensemble learning methods are examined: ordinary least squares, accelerated failure time, quantile regression (QR), support vector regression (SVR), K-nearest neighbor, random forest, adaptive boosting, gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) tree. The results indicate that statistical models perform better than machine/ensemble learning models in predicting train delays under incidents. The QR, SVR, and XGBoost tree models outperform other models in incident delay prediction in their respective methodological categories. The factors of the incident type and affected line type present the most significant effects on incident delay prediction in selected models
Correlates of perceived accessibility across transport modes and trip purposes: Insights from a Swedish survey
No full textAccessibility frequently serves as a central objective in policy and planning due to its significance for travel behavior, health, and economic development. However, until recently, there has been limited attention given to how individuals perceive their accessibility, and even less focus on how these perceptions differ across various transportation modes and trip purposes. This study explores how sociodemographic traits, accessibility indicators, as well as transport resources, attitudes, and habits correlate with perceived accessibility. We assess whether these correlations differ based on transport mode (bicycle, car, public transport, walking) and trip purpose (commuting, grocery shopping, dining out, reaching the city center). Based in the Gothenburg Region, Sweden, this study uses web-panel survey data to capture perceived accessibility. Sixteen ordinal regression models were applied, each tailored to a specific transport mode-activity combination. The results highlight the importance of all categories of correlates in shaping perceived accessibility. Specifically, transport resources, habits, and attitudes exhibit the strongest correlations. Notably, car access, positive car-related attitudes, and frequent car usage are linked to lower perceived accessibility for walking, cycling, and public transport but higher perceived car accessibility. Future studies should consider disaggregating their analyses based on travel mode, as significant disparities exist, particularly between perceived accessibility by car and alternative modes