Urban Travel Modes Research Articles

Tourists' urban travel modes: Choices for enhanced transport and environmental sustainability

This research investigates tourists’ urban travel mode choices in prominent Chinese tourist cities using latent class nested logit modelling. It identifies Comfort-Driven and Economy-Driven market segments, each exhibiting unique preferences towards transport-related factors as measured by elasticity and willingness-to-pay. Travel cost, walking time, waiting time, transfer time, crowdedness, and parking difficulty significantly affect tourists’ urban travel mode choices. Interestingly, car ownership and environmental consciousness have also been proven significant for both segments, while in-vehicle time is only significant for Comfort-Driven segment. The Comfort-Driven segment shows higher willingness-to-pay for reduced walking and transfer times, and prioritising comfort. Conversely, the Economy-Driven segment is more price-sensitive, focusing on value-for-money options that minimise both monetary and non-monetary costs. This study enhances the theoretical understanding of tourists’ transport service consumption and offers practical guidance with tailored strategies for encouraging low-carbon public transportation, hence it could facilitate the alignment of transport, tourism, and environmental sustainability goals.

Travel mode preference in Addis Ababa, Ethiopia: empirical observations

ABSTRACT This research paper analyzes urban travel mode preference in Addis Ababa using a survey distributed to 457 public transit users. The article highlights the details of individual travel and then discusses the implications of the travel patterns for transport policy in the city. The study employed percentages and run multinomial logistic regression to analyze the relationship between mode choice, travel characteristics and socio-economic features of individuals. In terms of mode choice and the influential factors, the analysis shows that income, age, travel expenditure and distance are the determining factors that affect mode choice. It was also found that the privately owned public transport, Taxi (mini-bus) and Higer (midi-bus) form the dominant mode of transport used by closely half percent (41.3%) of the survey respondents. On the basis of our analysis, we suggest that policies have to be devised to make government owned public transport service better in order to support a shift from using low occupancy vehicles to high occupancy vehicles which ultimately solve the problem of congestion and environmental pollution.

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.

Rebalancing Autonomous Vehicles using Deep Reinforcement Learning

The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.

Ride-Hailing Service Adoption and Local Context in Motorcycle-Based Societies: Case Study in Hanoi, Vietnam

The ride-hailing service (RHS) has emerged as a major form of daily travel in many Southeast Asian cities where motorcycles are extensively used. This study aims to analyze the local context in motorcycle-based societies, which may affect the establishment of travelers’ choice set after the appearance of RHSs. In particular, it empirically compares three types of choice-set structures in the context of urban travel mode choice by estimating standard logit and nested logit models to test six hypotheses on the associations of RHS adoption with its determinants. Revealed preference data of 449 trips from both RHS users and non-RHS users were collected through a face-to-face interview-based questionnaire survey in Hanoi, Vietnam, in December 2020. The results of model estimations revealed: (1) a substitutional effect for two-wheelers but not for four-wheelers, (2) a significant positive influence of car ownership on car RHS adoption but not on motorcycle RHS adoption, (3) significantly high sensitivity to travel time of motorcycle RHS but not of car RHS, (4) a significant negative effect of traffic congestion on car RHS adoption but an insignificant one on motorcycle RHS adoption, and (5) a significant positive association of an individual’s experience in using a smartphone with car RHSs but insignificant association with motorcycle RHSs. Our findings suggest that transportation policies of RHS motorcycles should be different from those of RHS cars because of the heterogeneity in travel behaviors of RHS users between them. They also indicate that the transition from motorcycles to cars as well as the difference in service availability among different types of RHSs should be incorporated into the development of transportation policies in Southeast Asian cities.

What Kind of Travellers Are Using Carsharing in Beijing? A Study Based on Selective Ensemble Learning

As a new urban travel mode, carsharing is significantly different from private cars, buses and other travel modes. Therefore, clarifying the typical characteristics of carsharing, such as individual users’ attributes, travel environment and travel behaviour, is conducive to accurately grasping the development of carsharing. In this study, a selective ensemble learning model is established to analyse typical travel characteristics of carsharing. Firstly, personal characteristics, environmental characteristics and behavioural characteristics were obtained through integrating order data, global positioning system data and station information. Then, based on a consolidated view of carsharing, different types of carsharing travel characteristics were distinguished using selective ensemble learning. Lastly, all kinds of carsharing travel are described in detail. It was identified through this research that carsharing travel can be divided into four kinds: long distance for leisure and entertainment, medium and short distances for business and commuting, a mixed category of medium and short distances for business and residence, and a mixed category of long distance for business and residence. This study can provide a theoretical reference and practical basis for precise planning and design and the scientific operation of carsharing.

Review of road surface photometry methods and devices – Proposal for new measurement geometries

Specifications concerning road lighting and photometry of road surfaces were established more than 50 years ago. Road lighting design and road marking visibility were developed for vehicle driving. The observation distance defined by standards corresponds to interurban applications; however, within Europe these areas do not tend to be lit. The objective of the SURFACE project is to propose new geometries for the photometric characterisation of pavements, both adapted to different urban travel modes and new lighting technologies. This article reviews the available guidelines, standards, measuring devices and literature regarding geometries and road lighting applications, and presents the project SURFACE analysis and proposal. The SURFACE consortium recommends adding several new angles for different driving conditions and road users; 2.29° for urban environments and consistency with road marking standard, and 1° for extra-urban environment and consistency with previous geometries. A 5° angle, corresponding to 17-m viewing distance, could be an interesting compromise, suitable for urban driving at low speed, cycling and for scooters. The angles of 10° and 20° are under consideration for describing the boundary between diffuse and specular behaviour.

Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data

Given the necessity to understand the modal shift potentials at the level of individual travel times, emissions, and physically active travel distances, there is a need for accurately computing such potentials from disaggregated data collection. Despite significant development in data collection technology, especially by utilizing smartphones, there are limited efforts in developing useful computational frameworks for this purpose. First, development of a computational framework requires longitudinal data collection of revealed travel behavior of individuals. Second, such a computational framework should enable scalable analysis of time-relevant low-carbon travel alternatives in the target region. To this end, this research presents an open-source computational framework, developed to explore the potential for shifting from private car to lower-carbon travel alternatives. In comparison to previous development, our computational framework estimates and illustrates the changes in travel time in relation to the potential reductions in emission and increases in physically active travel, as well as daily weather conditions. The potential usefulness of the framework was evaluated using long-term travel data of around a hundred travelers within the Helsinki Metropolitan Region, Finland. The case study outcomes also suggest that in several cases traveling by public transport or bike would not increase travel time compared to the observed car travel. Based on the case study results, we discuss potentially acceptable travel times for mode shift, and usefulness of the computational framework for decisions regarding transition to sustainable urban mobility systems. Finally, we discuss limitations and lessons learned for data collection and further development of similar computational frameworks.

The Response of Urban Travel Mode Choice to Parking Fees considering Travel Time Variability

Increasing automobile use leads to higher costs for traveling associated with emissions, congestion, noise, and other impacts. One option to address this is to introduce high parking charges to reduce the demand for automobile use and encourage the travel mode switch to public transport. To estimate commuters’ mode choice behavior in response to high parking fees, commuters from Nanjing completed an individually customized discrete choice survey in which they chose between driving and taking the bus or metro when choices varied in terms of time and cost attributes. Multinomial logit models were used to estimate commuters’ responses to high parking charges. In the models, the variability of travel times is considered and analyzed in the stated mode choice models. The results suggest that increases in costs of driving will lead to a great reduction in driving demand. The travel time reliability ratio is 0.50 and the value of each minute late is almost 5.0 times more than the average travel time with the restriction of the maximum allowed delays. The methods used in this study could be adopted to estimate the effect of variable pricing strategies on mode choice responses for different trip purposes. The high value given to travel time variability has implications for transport policy in terms of decision making with respect to new pricing strategies. Moreover, the valuation of travel time savings taken into account in this study would be helpful to better understand the effect of high parking fees.

Exploring transport perceptions across urban areas using free associations

In order to transform urban transport systems to get a potentially healthier and happier travelling public, it is important to understand how people perceive different urban travel modes, since this can affect their travel satisfaction, health and well-being. These perceptions were explored for five transport modes (walking, cycling, car driving, using bus and using train), in four areas in Birmingham, UK. The areas were chosen to reflect differing levels of deprivation and public transport provision. Data were collected using a postal questionnaire, which included a ‘free associations’ methodology. Respondents had to write down the first three associations that come to mind when thinking about each of the five modes, and subsequently rated how positive or negative each association felt to them. Perceptions of all modes except car driving differed significantly across the four neighbourhoods. The level of deprivation seemed to play a particularly distinctive role, as did the availability of public (rail) transport. There were strong relationships between the perceptions of a transport mode and its use, and sometimes also the use of other modes. Exploring perception using a free associations methodology provides insights into the strengths and weaknesses of transport modes as perceived by citizens from different backgrounds, as well as their predisposition to change modes.

A discrete choice analysis of transport mode choice causality and perceived barriers of sustainable mobility in the MENA region

Although there is considerable number of studies on urban travel mode choice, there are still two gaps: we have limited understanding of perceived and attitudinal barriers of sustainable modes and motives of personal car use, and the causes (not correlations) of mode choice decisions are almost unknown for certain geographical contexts such as the Middle East and North Africa (MENA). This study seeks to answer three questions: (1) what are the main barriers to choosing sustainable transport modes like active mobility and public transportation in the Middle East and North Africa? (2) which attitudinal or physical determinants define the transportation mode choice intentions and decisions in Tehran, Istanbul, and Cairo? and (3) what are the differences between the determinants of mode choice decisions in the case cities compared with those of Western societies? In this study, the data collected from 8284 interviewees in Tehran, Istanbul, and Cairo in 2017 were applied in a discrete choice model. The dependent variables of the modeling were the perceived main reasons against walking, biking, and public transit ridership, and the main factor encouraging car-driving. According to the findings, long walking distances, absences or lack of biking infrastructures, social and cultural problems and pressures against biking, and personal preference for cars compared to public transport prevent passengers from walking, biking, and using public transport. Comfort and convenience are the factors that make people avoid public transit in favor of cars. These determinants are fairly different from the main determinants of mode choice decisions in the Western societies. By applying a multinomial logistic regression model, 11 variables related to travel characteristics, perceptions, land-use and neighborhood, socio-economics, and self-selection were found significant or marginally significant in explaining all four models: the barriers to walking, biking, and public transit-use, and the motives for car-use. These findings support the hypothesis of this study that there are differences between the perceived and physical barriers to sustainable mobility as well as the motives of car-use in MENA megacities compared to Western societies. In short, mode-choice decisions and perceived determinants are context-sensitive. The conclusions of this study could be applied in urban and transportation planning in the MENA region to promote more sustainable mobility modes.

Electronic mobility market platforms – a review of the current state and applications of business analytics

In recent years, the number of urban travel modes has increased significantly and now includes services such as carsharing, e-hailing, ridesharing and bikesharing. This development potentially contributes to more sustainable urban mobility, but also creates complexity for the customer. To simplify customer offerings, so-called multimodal mobility platforms (MMPs) have emerged, bundling the different mobility services to find the best route for the user. These platforms also function as marketplaces where customers can purchase mobility services from different suppliers. As part of this process, data is being generated, which can be utilized to yield valuable insights for suppliers and platform operators. In this paper, we describe the business model of MMPs and provide an overview of currently active solutions. Subsequently we present specific use cases, showing how suppliers can leverage the analytics possibilities of MMPs and how this affects the business model.

Urban Travel Mode Split Optimization based on Travel Costs

With the rapid development of economy and accelerated pace of urbanization in China, the trip share of private cars has been increasing continually. This study investigates the optimal mode-split for a developing megacity and optimizes the weighted generalized travel cost per capita for one trip on an urban transport network. The main urban area of Beijing is taken as the study area of this research and the revealed preference survey method is utilized to get the trip survey data. Based on a nest-logit model, an optimization model is developed for the minimal weighted generalized travel cost per capita for one trip. The phase estimation method with the Newton-Raphson algorithm and the genetic algorithm are used to solve the optimization model. In addition, different cases are studied to assess the effect of different transport policies for the improvement of urban transport in Beijing. These policies are concerned with parking fee, taxi average fare, bus priority and rail transfer time. It is found that the bus priority policy for reducing the in-vehicle time of a bus trip has the greatest weighted generalized travel cost per capita for one trip in Beijing. Moreover, successful rail transfer time reduction is more beneficial to travellers in comparison to the effect of increasing parking fees of private cars or increasing the average fare of taxi utilization. In the future research, more comprehensive policy packages are worthy of studies in a further.

Analysing the Relationship Between Park-and-Ride Facilities and Private Motorised Trips Indicators

The current trend towards sustainable transport planning has led to various studies seeking strategies to reduce private motorised trip externalities in urban areas, but few studies that consider the effectiveness of park-and-ride facilities have been conducted. This paper introduces two models developed to evaluate the relationship between urban travel modes factors and infrastructure factors such as park-and-ride facilities as independent variables and duration and percentage of daily trips by private motorised vehicles as dependent variables. A set of data from 36 cities in different parts of the world was used to examine this relationship in various contexts. The strength of the relationships was estimated by multiple linear regression analysis. Based on the relationships, park-and-ride facilities are not significant factors in reducing the duration and percentage of daily trips by private motorised vehicles.

Transport Cost Analysis of City Bus and Private Car Usage in Johor Bahru, Malaysia

Researchers worldwide are engaged in the evaluation of the performance of urban travel modes. Economic cost comparison studies are commonly used as instruments to substantiate the most favored mode in urban transport. This paper attempts to provide a comparison of current usage of city buses and cars in the Johor Bahru town of Malaysia. Technical costs, social costs and environmental costs are compared in ensuring accessibility and mobility on one hand and reducing accidents and environmental losses on the other. The study relates to a stretch of 14.5 km of 6-lane divided carriageway road connecting Pontian in the North and Johor Bahru, Malaysia, and Singapore in the South. Traffic data and Transport costs were obtained via primary and secondary sources. When car usage is involved, vehicle operating costs (VOC) was found to be three fold (RM 2.05/ person-km) than the VOC of bus usage (RM 0.7/ person-km). Similarly, social costs, which mainly included the cost of accidents, amounted to 80 times the accident cost associated with buses. Environmental costs were modeled based on speed and total vehicle volumes for CO, NOx, SOx, and PM10 emissions and were expressed as total investment in pollution control. The environmental costs were then calculated in terms of medical costs believed to be caused by the pollution. These pollution costs amounted to 15 times the pollution cost associated with buses. The total transport costs were then obtained to provide comparative evaluation of the study modes. Furthermore, multiple future scenarios were created to provide cost analyses over time periods and modal split cases. In this regard, this paper provided a framework for a cost evaluation approach for an urban area. Results indicated that city buses are more cost-effective than cars, assuming equality in the number of passengers.

The automobile passenger—a forgotten mode

Current practices in urban transportation planning tend to neglect the automobile passenger. This study shows that a large number of urban trips are made as an automobile passenger and that the general characteristics of these passengers are similar to transit passengers rather than automobile drivers. The significance of these findings are discussed in terms of modelling the use of various urban travel modes.

Scaling perceptions of reliability of urban travel modes using indscal and factor analysis methods

In several attitudinal investigation, travel modes' reliability was found to be the most important characteristic of the transportation system. This research is concerned with individual subjective evaluation of different reliability attributes. Two scaling techniques—Indscal factors analysis—were used to investigate travel modes' reliability; these techniques aided in the identification performance measures of reliability. The scaling techniques were also evaluated and compared for thier usefulness and the reliability performance measures were validated using a preference model.

Urban travel alternatives: Models for individual and collective preferences

A method of estimating preference functions for alternative urban travel modes using non-metric scaling and conjoint measurement is introduced. The method treats travel alternatives as alternative collections of generic attributes and disaggregates preference orderings for alternative modes into components associated with the generic attributes. Preference functions are fitted for individual respondents and alternative methods of estimating collective preference functions for the group of respondents are examined. Particular attention is given to the error associated with aggregating individual responses. The methods are designed to be effective with relatively modest quantities of survey data.