Bus Mode Research Articles

A compact city for the wealthy? Employment accessibility inequalities between occupational classes in the London metropolitan region 2011

The prevalence of gentrification and housing marketisation processes in many cities points to increasingly wealthy inner-city areas and potentially greater population segregation by income. It is plausible that these trends are contributing to regional accessibility inequalities, though quantitative research testing this link is limited. This paper examines differences in employment accessibility between Standard Occupational Classification groups in the London Metropolitan Region for 2011 for car, transit, bus only and walking modes. Additionally, changes in occupational class populations 2006–2016 are considered, revealing continuing inner-city gentrification. Employment accessibility is calculated using cumulative measures, based on travel times from multi-modal network modelling. The results show that while car accessibility is relatively equal between occupational classes, public transport, bus and walk accessibility have significant inequalities favouring professional classes. Low income groups have lower accessibility for the most affordable bus and walk modes, and inequalities are greater for residents in the wider metropolitan region. Furthermore, professional groups combine accessibility advantages with the highest rates of owner occupation, maximising housing wealth benefits. Lower income groups are exposed to rent increases, though this is offset by social housing, which remains the most prevalent tenure in Inner London for low income classes.

Joint Models for Consideration of Public Transit and Mode Choice for Work Commute

The mode share of public transit has been decreasing in many Indian cities, while the ownership and usage of personal vehicles continue to grow. It is important to understand the reason behind this decline in transit ridership and frame policies to reverse this trend. Traditional mode choice models assume that all the modes are considered in the choice set while making a decision, which may not be realistic due to the following reasons. Individuals may not consider some modes due to the unavailability, lack of information, or infeasibility, or incompatibility with their travel and activity patterns. By ignoring this consideration process and assuming that all modes are available can lead to mis-specified models and erroneous policy evaluations. Thus, it is important to model both consideration and mode choice outcomes jointly. Accordingly, the main objective of this paper is to develop a joint model for three inter-related choice dimensions: (1) the consideration of bus, (2) the consideration of train, and (3) the primary mode for the home to work commute. These models are estimated using data from a sample of workers in Chennai city and the key explanatory variables influencing them are investigated. A positive correlation between the consideration of bus and train is found. The results also show that consideration of public transport modes influences the mode choice as expected. Furthermore, the coefficient of (log of) consideration probability on choice utility is significantly different from one (an assumption in many implicit availability/perception models). Also, some of the factors affected both the consideration and choice decisions, thus providing evidence that there is a partial (not complete) mediation effect of consideration on choice. Comparison with alternative models shows that neglecting these effects of consideration on choice can lead to biased coefficients, erroneous inferences, and poor model fit. The degree of vehicle availability and accessibility to transit stations exhibit varied effects in the choice of bus and train modes. In addition, their influence differs between the consideration and choice stages. The findings from this study could help decision-makers in developing separate policies for augmenting the consideration of public transit versus those that aim to retain or increase market shares.

Study of Selection of Bus Mode and Rail Train Surabaya – Malang

The need for transportation facilities and infrastructure that is quite large in the city of Surabaya to the city of Malang is the impact of population growth and increased activity in the city. To support economic, social, trade and education activities between the two cities, public transportation facilities are needed that can meet the needs of the community in terms of comfort and safety. Fulfillment of comfort factors in the use of public transportation such as cheap travel costs, speed of travel time, and accuracy of operational schedules, is expected to increase the interest of travelers to use public vehicles more often than private vehicles. The analysis method used, among others, Descriptive Analysis aims to identify the characteristics of users of the Surabaya-Malang and bus modes, then the Logistic Regression Analysis and Binomial Logit Difference Analysis aim to Obtain a model of selection of bus and train modes in the Surabaya-Malang route, then proceed Sensitivity Analysis which aims to determine the sensitivity of the model of the response of the traveler in determining the choice if there is a change in each attribute of the trip. The results of logistic regression analysis and binomial logit difference, it can be concluded that, attribute / variable X2 (travel time) is the attribute / variable that most influences the mode selection. The sensitivity analysis results can be concluded as follows: Sensitivity to travel costs, the possibility of respondents prefer to use the train mode is greater than the bus. Sensitivity to time, the possibility of respondents prefer to use the train mode is greater than the bus. Sensitivity to the departure schedule (headway), the possibility of respondents choosing to use the train mode will be greater than the bus, if the difference in the headway is between 90-165 minutes. However, if the difference in headway is above 165 minutes, then the respondent will switch to choosing the bus mode.

Study of Selection of Bus Mode and Rail Train Surabaya – Malang

Study of Selection of Bus Mode and Rail Train Surabaya – Malang

Visual representation in urban transport planning: Where have all the cars gone?

Abstract To strengthen public participation in planning and to aid the public in understanding the proposed plans, planners use different types of illustrations to show the outcome of a project. Effective presentation of the projects’ impacts on the public has become increasingly essential in planning and designing transport systems. However, a central premise for conscious decision-making is that the information of the project must be realistically presented so that the plans are accurate in relation to the estimated effects. Drawing from theories on visualisation in plans and science and technology studies (STS), the paper aims to discuss and raise awareness of visual representation in urban transport planning. Using data from large road infrastructure projects in Norway, visual illustrations found in planning documents are analysed in relation to written texts on traffic effects and the overall project goal. By focusing on the visual illustrations, it is shown that artefacts related to the green environment and transport modes of cycles, buses and walking are overrepresented in the documents. The visualisation support the idea of a sustainable transport system and a sustainable mobility planning approach. Most of the projects are road enlargement projects; however, there is a lack of visualisation of car flows, or the flows are very low. Cars are toned down (or even absent) compared with the estimated effects. The documents also show clear patterns of standardisation of artefacts. Nevertheless, there is a heterogeneity in the visual illustrations when combining different documents. The paper introduces the concept ‘add-on flexibility’ to illustrate this type of contextualisation. It contributes to a new critical perspective on visualisation and its representation in urban transport planning.

Personal and societal impacts of motorcycle ban policy on motorcyclists’ home-to-work morning commute in China

This study investigates the personal and societal impacts of motorcycle ban policy on the home-to-work morning commute of motorcyclists using self-reported travel behavioral data and stated travel mode shift response under such policy collected in Foshan City, China. The policy aims to force all motorcyclists in the city to shift to walk, bike, bus, or car mode by 2020. The complex impacts of the policy on travel mode shifts are studied across population sub-groups defined by gender and residential status (migrants and residents). For model estimation, random parameters multinomial logit models with heterogeneity in parameter means and variances were estimated to better track unobserved heterogeneity compared to their counterpart models with fixed means and variances. Model estimation results show that contributing factors of travel mode shift response include individual and household sociodemographic characteristics, travel-related behaviors, and residential location factors. In addition, out-of-pocket cost, opportunity cost of travel time, emissions, and energy consumption before and after motorcyclists would make their travel mode shifts and safety-related impacts were quantified and compared across the population sub-groups. The results show that motorcyclists, on average, would experience significant increases in their out-of-pocket cost and opportunity cost of travel time, particularly for male migrant motorcyclists. These results suggest that policy and infrastructural support for using public transit, walk, and bike modes, household mobility, and plan to purchase a car were likely to affect the personal and societal impacts of the motorcycle ban policy on travel mode shifts.

Consumer Perspective on Public Transport: The Medan-Pematang Siantar Route as a Case Study

Development of public transportation is meant to bridge the divide between cities and encourage development results. The inter-city transportation opens up opportunities for the inter-city trade, reduces costs and increases labor mobility, thereby promoting the inter-city development opportunities. This study uses the factor and cluster analyses. Analysis of factors is performed to determine the most dominant factors. The cluster analysis is used to identify the advantages and disdvantages of five modes of transport, namely taxi, trade rail, economy bus, mini bus, and executive bus. This study aims to determine the perception of users of Medan-Pematang Siantar route service. The results of this study show that there are five dominant factors that are considered before the service users decide on the mode of transport to use, namely the comfort factor, which is the most important consideration, followed by the time factor, cost factor, accessibility factor and safety factor.

A simulation study for evaluating taxi and bus mode in same-day delivery

Same-day delivery refers to the intra-city express of small goods, gifts and documents from sender to receivers. Same-day delivery currently adopts 'taxi' mode to complete the delivery as soon as possible with the sacrifice of lot scale economic benefits. Consequently, it is interesting that whether 'bus' mode featured with lot scale could dominate both in economic and delivery efficiency. To answer this question, simulation is employed to conduct a case study and compare the performance of 'taxi' mode and 'bus' mode. Further, two simulation scenario experiments are presented to find dominate conditions. Finally, we find that with limited distribution resources, 'taxi' mode can exploit advantages in the scenario of concentrated orders in space and low order volume, while 'bus' mode is more suitable for the scenario of wider delivery range and high order volume.

How sustainable is the growth of mass transit system in developing countries – an Indian perspective

In the modern urban world, transport researchers and policy makers are vehemently emphasizing the use of mass transit modes, primarily due to their distinct advantage in achieving congestion reduction, and for being environment friendly. In order to achieve high mass transit ridership, it is necessary to attract people to such modes so as to make them appealing and attractive when compared to personal motorized modes of transport. At the same time, it is also vital to assess different mass transit improvement alternatives and adopt a policy that leads to the sustainable development. India is currently witnessing a proliferation of new urban mass rapid transit systems, even in non-metropolitan areas. These are expensive to build and often do not realize their full potential even after decades of operations. This begs the question – can operational service quality improvement alternatives of existing public transit, provide users with benefits, comparable to a new mass rapid transit system? This paper evaluates the probability of modal shift to bus mode from non-mass transit modes on introduction of different service improvement measures applicable to Indian context. A multinomial logit mode choice model was developed based on a revealed preference dataset consisting of users’ travel behavior in existing traffic scenario where three mass transit modes – suburban rail, subway/ metro rail & bus, as well as the intermediate public transit mode of auto-rickshaw, where available. The model is used to predict impacts of three major policies - (1) existing network improvement, (2) lane management and (3) introduction of bus rapid transit system in terms of level of service improvements of bus transit and bus transit mode share. Results show that improvement in existing bus transit is more beneficial compared to lane management strategy and even comparable to bus rapid transit system considering the cost and time budget which may be used as a policy making tool by authorities for future city planning.

Optimal Multimodal Travelway Design for an Urban Street Network

This study introduces a bi-level model for optimal travelway design of an urban street network by successively executing a lower-level model for traffic assignments and an upper-level model for network travel time minimization. A computational experiment is conducted for optimal travelway design of a 4-square-km urban street network containing 25 signalized intersections, 80 street segments, and 5 bus routes that accommodates 62,640, 43,200, and 33,120 person-trips per hour in AM/PM peak, adjacent-to-peak, and off-peak periods, respectively. Model execution results indicate that adopting a higher number of narrow lanes for auto use only and auto/bus shared use could potentially lead to increases in auto mode share and savings of network total travel time. More narrow lanes for auto use could raise auto speeds, but the auto/bus shared use of narrow travel lanes could slightly fluctuate bus speeds. Further converting narrow lanes for shared use by autos and buses to exclusive bus lanes (EBLs) could enlarge bus mode share, reduce network total travel time, slightly elevate auto speeds, and drastically increase bus speeds. The proposed model could be augmented to incorporate optimization of networkwide intersection signal timing plans, bus signal priorities, and bus dispatching frequencies into optimal travelway design.

Better understanding the choice of travel mode by urban residents: New insights from the catchment areas of rail transit stations

The primary objective of this paper is to systematically and quantitatively analyze the peculiarities of the mode of travel chosen by residents. More specifically, this study focuses on two major aspects: (1) shedding light on the prominent factors that affect choice of travel mode from an innovative perspective of the catchment areas (CAs) of urban rail transit (URT) stations; and (2) mining and comparing the features and advantages of a diverse range of travel modes. Furthermore, it puts forward some proposals for optimizing the structure of the urban transportation system available to the residents. Using a valuable, screened and discretized, survey dataset of good size (10,385 travel activities of 4,080 individuals in 1,454 households in Nanjing, China), two mixed-logit (ML) models are established. One is based on trip origins/destinations within the CAs of the URT stations. The other is based on residence locations. The modeling results reveal that most residents, except those who own a car and/or driving license, are inclined to choose a slow mode of transport (including walking and cycling). Travelers do not notably err towards walking and bus modes during the peak hours and/or when going to work or school instead of cycling. Residents adjacent to the CAs of URT stations are attracted away from other travel modes and have a tendency to select non-motorized modes to access the URT (metro). The contrastive analysis and discussion results can help to provide urban planners, managers, decision and policy makers with useful suggestions and data support for exploring the various factors (household properties, individual attributes, and trip information) affecting residents’ choice of travel mode and seeking the residents’ travel rules. Moreover, our findings have important implications with respect to improving the structure of the choice of travel modes made available to residents and simultaneously be important in maintaining green, low-carbon, and sustainable development of urban traffic systems.

Particle exposure and inhaled dose while commuting in Lisbon

While commuting, individuals are exposed to high concentrations of urban air pollutants that can lead to adverse health effects. This study aims to assess commuters’ exposure to particulate matter (PM) when travelling by car, bicycle, metro and bus in Lisbon. Mass concentrations of PM2.5 and PM10 were higher in the metro. On the other hand, the highest BC and PN0.01-1 average concentrations were found in car and bus mode, respectively. In cars, the outdoor concentrations and the type of ventilation appeared to affect the indoor concentrations. In fact, the use of ventilation led to a decrease of PM2.5 and PM10 concentrations and to an increase of BC concentrations. The highest inhaled doses were mostly observed in bicycle journeys, due to the longest travel periods combined with enhanced physical activity and, consequently, highest inhalation rates.

Passenger flow forecast for customized bus based on time series fuzzy clustering algorithm

Abstract Customized bus services are conducive to improving urban traffic and environment, and have attracted widespread attention. However, the problems encountered in the new customized bus mode include the large difference between the basis of customized bus passenger flow data analysis and the basis of the traditional bus passenger flow data analysis, and the difficulty in different vehicle scheduling caused by the combination of traditional and customized bus modes. We propose a customized bus passenger flow analysis algorithm and multi-destination customized bus line capacity scheduling algorithm, and display them in an intuitive way. The experimental results show that the algorithm model established in this paper can basically meet the data requirements of operation and management, and can provide decision support for customized bus line planning.

MAC

Urban anomalies have a large impact on passengers' travel behavior and city infrastructures, which can cause uncertainty on travel time estimation. Understanding the impact of urban anomalies on travel time is of great value for various applications such as urban planning, human mobility studies and navigation systems. Most existing studies on travel time have been focused on the total riding time between two locations on an individual transportation modality. However, passengers often take different modes of transportation, e.g., taxis, subways, buses or private vehicles, and a significant portion of the travel time is spent in the uncertain waiting. In this paper, we study the fine-grained travel time patterns in multiple transportation systems under the impact of urban anomalies. Specifically, (i) we investigate implicit components, including waiting and riding time, in multiple transportation systems; (ii) we measure the impact of real-world anomalies on travel time components; (iii) we design a learning-based model for travel time component prediction with anomalies. Different from existing studies, we implement and evaluate our measurement framework on multiple data sources including four city-scale transportation systems, which are (i) a 14-thousand taxicab network, (ii) a 13-thousand bus network, (iii) a 10-thousand private vehicle network, and (iv) an automatic fare collection system for a public transit network (i.e., subway and bus) with 5 million smart cards.

The underlying effect of public transport reliability on users’ satisfaction

Service reliability has an important impact on the satisfaction stated by public transport users with the service they receive. The main source of unreliability is found in headway variance, which also affects waiting times and distributes passengers unevenly across vehicles. However, it is still unclear how headway irregularity, with its impact in waiting, crowdedness and reliability, affect travellers’ service satisfaction. Different stated preference studies have identified non-linear impacts produced by overcrowding. However, none of these studies is directly related to users’ satisfaction evaluation. In this study, we investigate the existence of this non-linearity in users’ satisfaction caused by both the crowding level and the number of denied boardings through a post-service satisfaction survey of bus and metro users. An Ordered Logit Model was estimated, accounting for sample heteroscedasticity and preference heterogeneity. Overall, there is a significant and negative perception of the bus mode, keeping all other attributes equal. For users under 35 years old, comfort experienced almost always plays an important role in service satisfaction, while for those over 35 years old women are significantly more sensitive to this attribute. Most important, crowding has a negative and non-linear impact on how passengers evaluate their travel satisfaction. Using a Likert-type scale, this curve is convex. This relationship between crowding and satisfaction might bias service planning and delivery if performance indicators associated to service are not properly weighted by the number of passengers served. Improving level of service indicators in this direction might provide public transport agencies a clearer and more accurate perception of the actual users’ experience.

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network

This paper describes a practical automated procedure to calibrate and validate a transit assignment model. An optimization method based on particle swarm algorithm is adopted to minimize a defined error term. This error term which is based on the percentage of root mean square error and the mean absolute percent error encompasses deviation of model outputs from observations considering both segment level as well as the mode level and can be applied to a large scale network. This study is based on the frequency-based assignment model using the concept of optimal strategy while any transit assignment model can be used in the proposed methodological framework. Lastly, the model is validated using another weekday data. The proposed methodology uses automatic fare collection (AFC) data to estimate the origin–destination matrix. This study combines data from three sources: the general transit feed specification, AFC, and a strategic transport model from a large-scale multimodal public transport network. The South-East Queensland (SEQ) network in Australia is used as a case study. The AFC system in SEQ has voluminous and high quality data on passenger boardings and alightings across bus, rail and ferry modes. The results indicate that the proposed procedure can successfully develop a multi-modal transit assignment model at a large scale. Higher dispersions are seen for the bus mode, in contrast to rail and ferry modes. Furthermore, a comparison is made between the strategies used by passengers and the generated strategies by the model between each origin and destination to get more insights about the detailed behaviour of the model. Overall, the analysis indicates that the AFC data is a valuable and rich source in calibrating and validating a transit assignment model.

Calibrating a Bayesian Transit Assignment Model Using Smart Card Data

Public transport planners can predict passenger loads and levels of service by applying the prior knowledge about the transit network and using transit assignment models. The individual travel history data available from automated fare collection (AFC) systems bring the opportunity of understanding the individual’s travel behavior, which is necessary to develop a transit assignment model. By combining the prior knowledge about the transit network with the AFC data, a transit assignment model can be calibrated. This paper proposes a Bayesian hierarchical model to estimate attributes of travel time components and to calibrate a transit assignment model. In this model, route choices are represented by a multinomial logit model, and its coefficients are estimated via a Markov chain Monte Carlo method. The proposed model is specified in two ways, and in order to consider travel time variability, it is assumed that travel time on links follows a gamma distribution. In the first specification, route choice variables and parameters are the same for all transit modes of bus, train, and ferry. In the second specification, mode-specific route choice variables and parameters are defined. In order to assess the model fitness, the root-mean-square error (RMSE) between each posterior estimate and the actual observation is computed. The lowest %RMSE belongs to the third-model specification (at 15%), which indicates its high predictive power.

Exploring the influences of compact development on zone-based travel patterns: a case study of the Seoul metropolitan area

ABSTRACTAlthough the influence of compact development on travel behavior has been extensively studied over the past decades, additional empirical evidence is needed to better understand that effect. This study explores the influences of the built environment (BE) on zone-based travel patterns using the 2010 Household Travel Survey data in Korea, . We apply seemingly unrelated regression (SUR) and censored regression models to reveal relationships between the BE and zone-based travel patterns in the SMA. Results indicate that: (1) transit-oriented design promotes public transit and suppresses auto trips; (2) the rail mode is more predominant relative to the bus mode; (3) mixed land use with high-density development and gridded pattern streets have a positive impact on walking; and (4) high density and easy access to rail have positive influences on reducing distance traveled by automobiles. Our results suggest that a differentiated strategy is needed to improve urban sustainability in the SMA.

An Analysis of Residents’ Commuting Behavior Considering Household Heterogeneity

In order to analyze the commuting behavior of residents more accurately, the quantitative family heterogeneity is integrated into commuting travel choice behavior analysis. Based on the data of residents’ travel survey in Xi’an, MNL models with neglecting household heterogeneity and Multi-Level MNL models with household heterogeneity are established respectively, and the parameters are calibrated. The model calibration results show that 19% of the residents’ commuting behavior selection is caused by household heterogeneity. Household heterogeneity cannot be ignored. Compared with the multi-layer MNL model, the MNL model overestimates the extent to which some explanatory variables affect the explanatory variables. At the same time, the analysis proves that having a car in the family is one of the key factors in inducing car travel. The transfer of bus and bicycle modes of transportation is highly accepted by commuters. We can increase the share of buses and reduce the use of cars by encouraging passengers to use bus and bicycle transfers.