Public Travel Modes Research Articles

Assessment of sustainable school transport policies on vehicular emissions using the IVE model

The increased vehicle demand leads to a rise in congestion, exhaust emissions and travel duration. It makes countries adopt policies to reduce vehicular exhaust emissions to achieve sustainable development with improved air quality. The present study identifies factors that influence student's preferences, estimate vehicular emissions and examine policies to minimize use of passive school transport (PST). The responses of 945 students are statistically examined using the Multinominal Logit (MNL) model with factors that affect preferences of school transport. The MNL model revealed that private transports are preferred more by female students. Students covering up to 3 km distance preferred more to use active school transport (AST) than students with travel distance of above 5 km. The model has predicted that students with travel distance of 4 km or above are mostly opted for public travel modes. The representative driving cycle was developed for morning and afternoon school trips and used as an input parameter for the International Vehicle Emission (IVE) model to estimate exhaust emissions. School policies are proposed to reduce PST modes associated student kilometer travelled (SKT) and emissions. The study outcomes revealed that neighborhood school policy predicted significant reduction in yearly SKT for AST (6.07%) and PST (36.03%) modes. While increased walkability policy anticipates modest impact in minimizing the PST mode associated total exhaust emissions (3.18–40.34%). The private vehicle associated SKT and total exhaust emissions are reduced by nearly 27.58% and 34.66% under increased use of public transport policy respectively.

Day-to-Day Dynamic Traffic Flow Assignment Model under Mixed Travel Modes Considering Customized Buses

In recent years, customized buses (CBs), a new form of public travel mode between bus and car, has sprung up in China. Its characteristics include flexible routes, each person having a seat and point-to-point travel have attracted travelers who seek high-quality travel, especially car travelers, alleviating traffic congestion at peak periods and leading to a change in urban travel modes. In addition to providing new travel modes, an exclusive bus lane (EBL) is also an effective means to alleviate traffic congestion. Therefore, this paper establishes link impedance functions under mixed travel modes considering the EBL, including customized buses on different kinds of links, and then presents a day-to-day dynamic traffic flow assignment model based on stochastic user equilibrium (SUE). Some conclusions were summarized by numerical case studies. First, the parameter of travelers’ sensitivity to route travel time affects the speed of traffic flow evolution. When it increases to positive infinity, the final state of the traffic network moves from SUE to deterministic user equilibrium (DUE). Second, the parameter on the degree of dependence of travelers on previous experience can not only influence the value size of actual travel time, but also influence the direction of actual travel time evolution. Third, conventional buses and customized buses have higher transportation efficiency than cars, but if the proportion of conventional bus travelers is too large, the total travel time of all travelers in the traffic network may increase. Fourth, when travel demands increase, the proportion of travelers who choose public transit is required to increase to achieve minimum total travel time. Lastly, from the perspective of the whole traffic network in any case, the EBL is not always beneficial. It is recommended to set EBLs when conventional buses and customized bus flows are heavy, which can be judged based on the model established in this paper.

EXAMINATION OF METRO STATIONS PEDESTRIAN ACCESSIBILITY FOR ALL USING OPEN DATA KIT: A CASE STUDY OF NOIDA CITY IN INDIA

Abstract. To make transit rail services more attractive, the preferred public travel mode and serve as the backbone of any metropolitan area, there arises a need to create a sustainable and inclusive urban environment with access to all. This study aims to map the pedestrian permeability and impermeability by categorizing roads and streets around identified transit nodes in Noida as public, private and non-accessible by all. By doing so, the study identifies very distinct and spatial divisions like gated communities and urban villages that have emerged as social ghettos in the planned city Noida and thereby, devoid it of being an inclusive city. Initial metro station smart android-based mobile surveys were done across 12 elevated metro stations falling on the blue transit corridor line connecting Delhi with its satellite town Noida using smart apps namely ODK and Kobo Toolbox with the analysis done in QGIS. It was found that trip distance and time had a strong association with walking while age, gender and purpose of the trip had no association with walking to the metro station. Most transit users living within one-kilometer walking distance prefer to walk to the nearest metro station in Noida. Finally, the study shows the area difference between the perceived and actual Isochrones prepared in QGIS as Ped-sheds after deducing areas such as gated communities, superblocks and unplanned settlements like urban villages where the existing road/street network is not accessible by all or safe to use.

Effects of tube service disruptions on the usage of the London Cycle Hire

Urban rail transit and cycle hire are two main public travel modes used in London. However, their relationship, in terms of substitutability and complementarity has not yet been fully studied. This paper investigates the effects of tube service disruptions on the usage of the London Cycle Hire (LCH) using the data for more than one million LCH trips during the period from April 20th to May 22nd, 2019. The tube service status data were collected from Transport for London (TfL) over the same period. Covariates including socio-economic information, built environment characteristics, and weather conditions are also controlled for in the analysis. A buffer-area based panel data regression model is used to estimate the effects of tube service disruptions on the LCH usage. Our results indicate that the closure of tube stations can reduce the number of nearby LCH trips during the peak times, and increase the number of LCH trips during the inter peak times. Severe delays and suspensions can both lead to an increase in LCH departure trips in the morning peak, but the effects are not statistically significant during the evening peak or inter peak times. Moreover, compared to the effects of tube station closures, the effects of severe delays and suspensions are much smaller. This study provides a better understanding of the interdependencies between the urban rail transit and public bicycle share systems.

Word of mouth and behavioural intentions of the automated bus service

Automation technology has received great attention recently and been expected to play an important role in future transport systems. Understanding determinants of travellers' intention to use the bus and recommend the service to others is critical to promote such new travel modes. Although previous studies have focused on plausible scenarios based on hypothetical behavioural reactions, predicted adoption intention and recommendation behaviour may not be consistent with those exhibited when the users are exposed to the real automated bus service. Using a panel survey conducted in Stockholm, where an automated bus services is currently operating in the mixed traffic environment on the public road, this study addresses this research gap by investigating public acceptance and usage of these automated buses. Applying a behavioural model, this study explores key influencing factors of automated bus use and word of mouth intention. The results indicate that users' needs and travel demands have a strong power to influence people's intentions to use such a new public travel mode and recommend the service to others. Furthermore, individuals' behaviour intention is found to have no significant effect on actual behaviour in this study, which suggests a gap between intention and action.

A Study on the Decay Model of Multi-Block Taxi Travel Demand under the Influence of Major Urban Public Health Events.

A sudden major public health event is likely to have a negative impact on public transport travel for residents, with public travel modes such as the metro and conventional buses experiencing varying degrees of decline in patronage. As a complement to public transport, taxi travel will suffer the same impact. Land use and population density among various functional blocks in a city are different, and therefore their changing rates in taxi travel demand are varied. This paper reveals the taxi travel demand correlations between urban blocks and then constructs a taxi travel demand decay model based on the Dynamic Input-Output Inoperability Model (DIIM) to simulate the decay degree of taxi travel demand in each block. When a major public health event occurs, the residential panic levels in different functional blocks may vary. It results in variable changing speeds of residential travel demand in each block. Based on this assumption, we use the intensity of travel demand as a correlation strength factor between blocks, and equate it with the technical coefficient in the DIIM model. We also define other variables to serve in model construction. These variables include the decay degree of travel demand intensity, residential travel willingness, coefficient of travel demand decay, derivative coefficient of travel demand interdependency, and demand perturbation coefficient. Lastly, we select a central area of Ningbo as the study area, and use taxi travel data in Ningbo during the COVID-19 pandemic of 2020 as input, simulate taxi travel demand dynamics, and analyze the accuracy and sensitivity of the model parameters. The relative errors between the five types of blocks and the actual decay of travel demand intensity are 8.3%, 3.8%, 8.7%, 5.5%, and 5.3%, respectively, which can basically match the actual situation, proving the validity of the model. The results of the study reveal the pattern of taxi travel demand decay among various blocks after major public health events. It provides methodological reference for decision makers to understand the development trend of multi-block taxi travel demand, so as to help form effective emergency plans for different blocks.

Emergency Response for COVID-19 Prevention and Control in Urban Rail Transit Based on Case-Based Reasoning Method

As an important public travel mode, urban rail transit has the characteristics of crowded passengers and closed operation. Safe management of urban rail transit is an important research topic that attracted attention in recent years. This article proposes a decision analysis method based on case-based reasoning, which aims to solve the emergency response problems for the prevention and control of corona virus disease 2019 (COVID-19) in urban rail transit. In this method, first, the historical cases are extracted and filtered by calculating the similarity between the target case and the historical case. A set of similar historical cases is constructed by setting the similarity threshold in advance. Second, comprehensive utility value of emergency response of each similar case is calculated referring to the utility evaluation of emergency response effect and response cost of each similar historical case. On this basis, the emergency plan of the target case is generated by selecting the emergency plans of the similar historical cases corresponding to the maximum comprehensive utility values of the emergency responses. Finally, with the emergency responses of COVID-19 in Tianjin rail transit as the background, this paper explains the feasibility and effectiveness of the proposed method within a case study.

Travel Mode Identification With GPS Trajectories Using Wavelet Transform and Deep Learning

Accurate identification in public travel modes is an essential task in intelligent transportation systems. In recent years, GPS-based identification is gradually replacing the conventional survey-based information-gathering process due to the more detailed and precise data on individual’s travel patterns. Nonetheless, existing research suffers from deficient feature selection, high data dimensionality, and data under-utilization issues. In this work, we propose a novel travel mode identification mechanism based on discrete wavelet transform and recent developments of deep learning techniques. The proposed mechanism aims to take GPS trajectories of arbitrary lengths to develop accurate travel mode results in both global and online identification scenarios. In this mechanism, raw GPS data is first pre-processed to compute preliminary motion and displacement attributes, which are input into a tailor-made deep neural network. Discrete wavelet transform is also adopted to further extract time-frequency domain characteristics of the trajectories to assist the neural network in the classification task. To evaluate the performance of the proposed mechanism, a series of comprehensive case studies are conducted. The results indicate that the mechanism can notably outperform existing travel mode identifications on a same data set with minuscule computation time. Furthermore, an architecture test is performed to determine the best-performing structure for the proposed mechanism. Lastly, we demonstrate the capability of the mechanism in handling online identifications, and the performance sensitivity of the selected attributes is evaluated.

Expanding the Theory of Planned Behaviour to Reveal Urban Residents’ Pro-Environment Travel Behaviour

Exploring the mechanism that influences the choice of urban public travel mode is an important policy research topic that can promote urban residents’ pro-environment travel (PET) behaviour and relieve the pressure on urban traffic and environmental problems. By expanding the theory of planned behaviour by considering the effects of the quality of public transport service and individual behaviour, this paper establishes a mixed PET behaviour model. Grounded theory is used to analyse data obtained from in-depth interviews, with the aim of determining the relationships among different attributes of the quality of public transport service and PET. An empirical examination in the form of a questionnaire was conducted in Changsha, China, to obtain the intensity and mechanism of various factors influencing pro-environment behaviour decision-making. The results reveal three new pieces of information. First, the influence of many psychological variables (except subjective norms) is consistent with the prediction results of the theory of planned behaviour (TPB), and the predictions of the model are accurate. More specifically, intention (0.535) and habit (0.354) are key factors in PET behaviour, while attitude (0.527) has the most significant effect on intention towards PET behaviour. Second, the perceived service quality of public transport has a direct and significant impact on intention towards PET behaviour. Satisfaction with public transport service quality exerts a mediating effect on perceived service quality and PET behaviour. More specifically, operation and management (0.808) and vehicle environment (0.809) have the most important influence on intention towards PET behaviour. Last but not least, the extent of the influence of PET behaviour varies based on travellers’ demographic characteristics. The driving age, income and ownership of private cars show the greatest impact. The perceived service quality of public transport and travellers’ social and economic characteristics all play roles in the psychology of travel decisions, and are associated with PET behaviour on several distinct levels. From the perspectives of passenger psychology, public transport service quality and personal attributes of passengers, this paper provides a scientific basis for decision-making in transportation systems and the formulation of traffic intervention strategies to promote voluntary public reductions in carbon-intensive travel behaviour.

Judgement of Transport Security, Risk Sensitivity and Travel Mode Use in Urban Areas

The current study aims to examine judgement of security in public transport and, more specifically, the role of the priority of security and risk sensitivity in the use of public travel modes versus car among an urban public. The results are based on a self-completion questionnaire survey conducted among residents above 18 years of age in the six most urbanised areas in Norway (n = 1043). The respondents were randomly obtained from the Norwegian population registry. The results showed that priority of security as well as risk sensitivity was significant predictors of travel mode use among an urban public when demographic factors were controlled for. In studies carried out previously, risk sensitivity was conceived to be a predictor of risk perception. The large proportion of explained variance in perceived risk reported in previous studies could be partly due to the use of risk sensitivity as a predictor variable, which is coincident with the criterion variable. It is suggested that the risk perception concept could be replaced with perceived risk evaluations, which cover the intuitive cognitive judgements of probability of an event with negative consequences as well as the severity of consequences if such an event takes place. It is proposed that risk sensitivity could be the main concept, covering the perceived risk evaluations, including intuitive judgments of probability as well as severity of consequences across a set of risk sources.

Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes

Emerging autonomous vehicles (AVs) and shared mobility systems per se will transform urban passenger transportation. Coupled together, shared AVs (SAVs) can facilitate widespread use of shared mobility services by providing flexible public travel modes comparable to private AV. Hence, it may be conjectured that future urban mobility is likely an on-demand service and AV private ownership is unappealing. Nonetheless, it is still unclear what observable and latent factors will drive public interest in (S)AVs, the answer to which will have important implications on transportation system performance. This paper aims to jointly model public interest in private AVs and multiple SAV configurations (carsharing, ridesourcing, ridesharing, and access/egress mode) in daily and commute travels with explicit treatment of the correlations across the (S)AV types. To this end, multivariate ordered outcome models with latent variables are employed, whereby latent attitudes and preferences describing traveler safety concern about AV, green travel pattern, and mobility-on-demand savviness are accounted for using structural and measurement equations. Drawing from a stated preference survey in the State of Washington, important insights are gained into the potential user groups based on the socio-economic, built environment, and daily/commute travel behavior attributes. Key policies are also offered to promote public interest in (S)AVs by scrutinizing the marginal effects of the latent variables.

What Should Shanghai Do in Urban Transformation? Experience in Six Typical Cities

Abstract Cities in China are facing a critical period of development model transformation. Shanghai, Beijing as the representative of the big cities have carried out useful explorations in the transition path, but still at the initial stage, and lack of theoretical support as well. In this paper, research will be conducted in a historical and comparative perspective. Six typical cities will be involved, they’re cities from developed countries, such as London, Tokyo, Singapore, New York, and cities from developing countries, such as Beijing and Shanghai. After analyzing the changes of several important demand and supply indexes in the past fifty to hundred years, the transition process in several typical big cities can be concluded from the value changes in system characteristic indexes. All the four cities in developed countries have found their way out. They have different features from each other. Tokyo rely on the subway. Singapore release different policies at different times, the government can control the number of the vehicle ownership. In Greater London, cycling is an emerging travel mode, besides, public travel mode has been encouraged in various ways… Finally, put forward corresponding suggestions or opinions on the future development of the transportation in Shanghai, especially the direction of the transformation.

Objective and Perceived Risk Estimates of Travel Modes in the Norwegian Public

ABSTRACT The aim of this study was to identify groups of travel mode users, based on objective risk estimates, and examine overall differences in demographic characteristics, perceived risk, worry, perceived control when using travel modes, trust in authorities, and safety motivation. The results were based on a self-completion questionnaire survey about risk perception and travel mode use in a representative sample of the Norwegian public (n = 1864). In addition, aggregate-level data on accidents in transport were used to establish the “objective risk” for various travel modes. The respondents were split into two clusters. The first cluster was characterized by a relatively greater objective risk for accidents related to public travel modes as well as related to being a pedestrian, while the second cluster was characterized by a higher risk level related to motorized private modes of transportation. There was a significant overall difference in the risk estimates among the members of the two clusters. There was also an overall difference in risk perception and other risk-related judgments due to which risk estimate-based cluster the respondents belonged. Associations between objective risk estimates, perceived risk, and worry are discussed in relation to cluster differences in objective risk.

AN INTEGRATED MODELING FRAMEWORK FOR ENVIRONMENTALLY EFFICIENT CAR OWNERSHIP AND TRIP BALANCE

Urban transport emissions generated by automobile trips are greatly responsible for atmospheric pollution in both developed and developing countries. To match the long-term target of sustainable development, it seems to be important to specify the feasible level of car ownership and travel demand from environmental considerations. This research intends to propose an integrated modeling framework for optimal construction of a comprehensive transportation system by taking into consideration environmental constraints. The modeling system is actually a combination of multiple essential models and illustrated by using a bi-level programming approach. In the upper level, the maximization of both total car ownership and total number of trips by private and public travel modes is set as the objective function and as the constraints, the total emission levels at all the zones are set to not exceed the relating environmental capacities. Maximizing the total trips by private and public travel modes allows policy makers to take into account trip balance to meet both the mobility levels required by travelers and the environmentally friendly transportation system goals. The lower level problem is a combined trip distribution and assignment model incorporating traveler's route choice behavior. A logit-type aggregate modal split model is established to connect the two level problems. In terms of the solution method for the integrated model, a genetic algorithm is applied. A case study is conducted using road network data and person-trip (PT) data collected in Dalian city, China. The analysis results showed that the amount of environmentally efficient car ownership and number of trips by different travel modes could be obtained simultaneously when considering the zonal control of environmental capacity within the framework of the proposed integrated model. The observed car ownership in zones could be increased or decreased towards the macroscopic optimization objective with zonal limit of emissions.