Mode Choice Decisions Research Articles

Escorting and mode choice decisions of members of an urban university in New Delhi, India

This paper explores the escorting and mode choice decisions of members – faculties, staff, and married students – of an urban university in New Delhi, India. The decisions relating to school travel of children of members of a university are captured using a primary travel behavior survey. The data is further segmented based on residence location of university members, i.e., on-campus and off-campus housing. Descriptive analysis is followed by a multinomial logit model, employed to study the escorting person and mode choice decisions. This study finds similarities and differences in behaviors. Respondents staying inside the campus own more cars and motorcycles as compared to the respondents staying outside the campus. However, those staying outside possessed more bicycles than the respondents living inside the university. Further, irrespective of the location of residences, parents holding higher educational qualifications are reluctant to walk/bicycle their children to school. For on-campus residents, household car (and two-wheeler) ownership leads to fathers using personal cars for pick-up and drop off children. Household car ownership also negatively influences mothers' intentions to use active modes for child escorting. Further, for both on-campus and off-campus residents, even if households perceive the environment to be safe and secure, they have a higher dependency on private vehicles, especially by mothers. This study also estimates aggregate elasticity effects of relevant variables and discusses, from a broad viewpoint, the policy implications of the findings.

Responsive make‐to‐order supply chain network design

AbstractIn this article, we address two network design problems for a responsive supply chain that consists of make‐to‐order (make‐to‐assemble) facilities facing stochastic demand and service time. The response time to customer orders, critical to the success of the supply chain, is the sum of the flow time in the facility and the delivery time to the customers. The response time performance in our models is measured by the probability that the response time is shorter than a constant. This nonlinear performance measure makes the models less or not tractable. The objective of both problems is to minimize the expected network cost that consists of the cost of delivery to customers as a function of the time of delivery (or the mode of transportation), the fixed cost of locating facilities and the capacity cost as a linear function of the processing capacity. The main decision variables are the number and locations of facilities, the resources allocated to them and the delivery mode selected between facilities and demand. In the first problem, a constraint is imposed to ensure an acceptable response time level. In the second problem, a penalty is charged on the number of days that each unit is delivered later than the targeted response time and is incorporated into the objective function. In the first problem, the probabilistic constraint on the flow time can be linearized and the problem can be formulated as an integer linear programming model. In the second problem, we propose an approximation approach to linearize the objective function and an iterative search and cut algorithm to combine linear approximation with neighborhood search. A multi‐start meta‐heuristic is also suggested. Computational experiments are conducted to evaluate the performance of these solution procedures. Recommendations are made on the basis of the computational results. This appears to be the first study in the area of locating capacitated facilities with stochastic demand to incorporate the delivery mode choice decision and to evaluate the expected congestion cost as a function of the actual flow time in the facilities, instead of the average one. The models developed enable the decision maker to investigate the effect of a combination of facility location, capacity allocation, and delivery mode decisions on the expected network cost and the response time. In addition, the findings of the computational experiments shed light on tuning parameters of approximation algorithms.

The role of personal norms in the choice of mode for commuting

Research in travel behavioural studies have shown that social forces play a significant role in travel decision making like socio-economic factors. Several studies have tested models with different subjective variables to investigate their effects on travel behaviour. Understanding the influence of social norm and personal norm on travel mode choice preference is essential in promoting sustainable travel, considering the reported importance of these social forces in decision-making. This study draws upon the theories underpinning these sociological constructs and the integrated choice and latent variable (ICLV) framework to develop an ICLV model by incorporating social norm and personal norm as latent variables to investigate their impact on transport mode choice decisions. The results of the ICLV model is consistent with the findings in similar studies and extend the literature in transport mode choice modelling. The results indicate that internalised norms or personal norms have a significant influence on individual behaviour, and positively influence travel behaviour. This strengthens the claim that activated norms and pro-environmental behaviour (PEB) influence overt behaviour by inducing a sense of obligations to act. The results also suggest that individuals with pro-environmental attitude are likely to travel with sustainable travel modes. Thus, reinforcing the importance of sociological and psychological factors in decision-making. Unlike studies involving social norms and personal norms, this research is novel because it applies the ICLV framework to empirically investigate the impact of these sociological constructs on travel mode choice behaviour by incorporating them as latent variables in an ICLV model. The contribution of this study to the literature is that it shows that personal norm has a substantial positive impact on sustainable travel. The impact of this study could be situated in the framework of sustainable transport. The findings are relevant for policymaking, the development of policies meant to create awareness of the consequences of travel behaviour could promote the development of strong personal norms, and consequently influence travel decision making. This can be useful for promoting sustainable travel as the findings shed light on the characteristics of individuals most likely to travel by active modes or PT.

Mode choice behaviour of students, integrating residential location characteristics: a study from Kochi City, India

Mode choice decision of individuals plays a vital role in transportation planning. Individual travel behaviour models can be improved by extending the set of influencing variables used for modelling. In a developing country like India, students contribute a major share of total travel demand especially during morning and evening peak hours of traffic; whose individual travel characteristics are very less studied by transportation professionals. This paper presents exploratory and statistical analysis of mode choice behaviour of students in Kochi City, India. The socio-demographic characteristics, activity-travel behaviour as well as residential location characteristics of students during a usual working day is collected using activity-travel survey data, in Kochi Municipal Corporation. Preliminary analysis gives details on daily activity-travel pattern, mode choice preferences and other particulars of commuters in the study area. Statistical models were developed for understanding the factors affecting mode choice decision and separate mode choice models are also developed for different categories of students. Simulation of choice probabilities over different attributes is also done to identify the potential policy variables that can promote the use of sustainable modes.

Incentivised Travel and Mobile Application as Multiple Policy Intervention for Mode Shift

Transport is one of the influential aspects of a person's day-to-day life, as their activities cannot be fulfilled without moving. To expect a person to change their travel behaviour, from their routine or regular pattern, involves major decisions that are subject to a weighting process of gain and loss. While the decision to cancel a trip and choose an alternative travelling route is much easier to call, mode shift to public transportation is way more complex and requires encouraging factors to make it happen. The objective in this study is to investigate the impact of incentive programmes and mobile applications on mode shift. A conceptual structural equation model (SEM) is developed based on questionnaires to analyse the conceptual model by virtue of applying statistical methods. The model results have indicated that the simultaneous implementation of mixed mode strategies are more effective in encouraging travel mode choice decisions. These strategies are push-pull approach (PPA) and pull-information approach (PIA). The right formula between incentive programmes and push factors can encourage behavioural change in mode choice (PPA strategy), whereas the pull or soft policies with the right travel application can be vital in mode choice (PIA strategy). In terms of significance, the PIA is twice the significance of PPA.

Examining human attitudes toward shared mobility options and autonomous vehicles

This paper presents a comprehensive analysis of people’s attitudes toward shared mobility options and autonomous vehicles (AVs), with a focus on the underlying patterns and potential determinants. A stated preference (SP) survey was designed and implemented in the U.S. Four sets of questions were included in the questionnaire, each focused on one unique aspect of user attitudes, including a) preferences for mobility options and lifestyle (such as overall view of driving, factors in mode choice decisions and technology engagement), b) perceived benefits and concerns of shared mobility option, c) reasons toward or against private vehicle ownership, and d) motivations for and desired features of AVs. A structural equations model was developed to identify latent attitudinal factors and examine the correlations between the latent attitudes (as the endogenous variables) and the observed covariates (including the socio-economic and demographic characteristics, and users’ current mobility profile, such as mode use frequency, travel distance, and trip fare). The model identified eleven latent factors that represent various aspects of attitudes toward AVs and shared mobility options. The findings could be used by policymakers and Transportation Network Companies (TNCs) to a) recognize the users’ latent attitudes, b) understand the underlying patterns of attitudes, c) implement plans and policies more efficiently, d) guide or influence users’ perceptions, and e) enhance travel behavior models. This study lays the foundation for further analysis on understanding user acceptance and adoption of these emerging mobility options, which is essential to estimate the likelihood and magnitude of behavior shifts in the era of automated, connected and shared mobility.

Modeling the effect of Mobility-as-a-Service on mode choice decisions

ABSTRACT Mobility-as-a-Service (MaaS) can be considered the latest innovative technological solution to sustainable transportation. It offers travelers access to a pre-paid bundle of transportation modes on a single app with the additional convenience of the integration of planning, booking, and payment. Capturing the impact of this new technology on travel behavior remains a challenge, given the scarce number of comprehensive MaaS pilots. In this study, a stated adaptation experiment was designed to determine travelers’ propensity toward using this new service. Such knowledge sheds light on the potential of MaaS to alter daily travel patterns.

Machine learning applications in activity-travel behaviour research: a review

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

Interrelationships between Mode Choice and Trip-chain Choice decisions: A case of Mumbai Metropolitan Region

Mumbai is one of the major cities in India, where the traffic volume is constantly being increased because of urbanization trend and changes in the socio-economic status of the society. These conditions not only influence commuter’s travel behaviour but also making it too complex. Mode choice and trip-chain choice are the two critical factors influencing this complex travel behaviour. In practice, different activity-based models are using different relationship patterns due to lack of consensus and proper empirical evidence. A few studies focus on the directionality of the trip-chain and mode choice decisions. Hence, this study investigates the hierarchical relationship between these two choice decisions including the influences of socio-demographic characteristics on them. This study uses Structural Equation Models for capturing this multidirectional relationship. This study uses a 15-day activity-travel data and activity-travel information obtained from the survey for defining the choice set. Two separate models are estimated for weekdays and weekends. From the model results, it was observed that the mode choice precedes trip-chain choice during weekdays and mode choice and trip-chain choice decisions are simultaneous during weekends. A number of socioeconomic characteristics also play major roles in influencing the relationships. The model results presented in this study are based on the individual level observations. Hence, for future research, it is necessary to incorporate household interactions and to study the relationship between these choices at the household level.

Establishment mode choice of Finnish SMEs in Estonia: a case study approach

In this article, we reveal how and why firm level and institutional distance influence the entry mode choice of Finnish SMEs in Estonia. This paper investigates the impact of firm characteristics and institutional distance on establishment mode choice decision of Finnish SMEs in Estonia. We use case study approach based on in-depth interviews with decision makers of SMEs operating in Estonia and follow-up interviews in order to deepen results of the first research stage. The results shows that formal institutional factors combined with firm level factors affect the decision of establishment mode choice in Estonia, while informal institutions have not had significant effect on choice between greenfield vs. acquisition in case of Finnish SMEs. Overall, our study contributes to a more nuanced view of choice of foreign market entry mode of SMEs and highlights future research opportunities that exist for considering strategy imitation and its implications in international business research.

Bike-sharing acceptance through the lens of the theory of routine mode choice decisions

This paper aims to provide insight into factors affecting bike-sharing acceptance among Malaysians. Through a quantitative survey, data were collected from 120 commuters residing in the Klang Valley. The findings indicated that safety, comfort, and enjoyment are the decisive determinants of bike-sharing acceptance. Meanwhile, availability and costs have no significant impact. Based on the findings, bicycle distributors and competent authorities in Malaysia should focus on improving the comfort of service, bicycle infrastructure, and network that would support the growth of bicycle usage, especially in urban communities. The theory of routine mode choice decisions served as the underpinning theory to predict bike-sharing acceptance. Since limited studies have been carried out on the Kuala Lumpur public bicycle-sharing program, this study is among the first to contribute on the subject. The current study provides a platform for future empirical bike-sharing studies in Malaysia as a greenhouse-friendly and sustainable transportation mode.

Influence of psychological factors in mode choice decision making: A structural equation modeling approach

This study investigates the influence of psychological factors in the mode choice of private or public transport among commuters. Theory of Planned Behavior (TPB) was adopted as a theoretical basis for incorporating the influence of psychological factors in mode choice decision making. TPB is a theory designed to predict and explain human behavior in specific contexts. Attitude, subjective norm, perceived behavioral control and intention are the factors considered in TPB. A questionnaire survey was conducted among 400 commuters in the city of Calicut, Kerala, India. Structural equation modeling was adopted to test the relations stated in the Theory of Planned Behavior. ‘Attitude’ was identified as the most significant factor influencing the intention to use public transit. The results of the study are useful to obtain a better understanding of mode choice behavior.

Bike-sharing acceptance through the lens of the theory of routine mode choice decisions

This paper aims to provide insight into factors affecting bike-sharing acceptance among Malaysians. Through a quantitative survey, data were collected from 120 commuters residing in the Klang Valley. The findings indicated that safety, comfort, and enjoyment are the decisive determinants of bike-sharing acceptance. Meanwhile, availability and costs have no significant impact. Based on the findings, bicycle distributors and competent authorities in Malaysia should focus on improving the comfort of service, bicycle infrastructure, and network that would support the growth of bicycle usage, especially in urban communities. The theory of routine mode choice decisions served as the underpinning theory to predict bike-sharing acceptance. Since limited studies have been carried out on the Kuala Lumpur public bicycle-sharing program, this study is among the first to contribute on the subject. The current study provides a platform for future empirical bike-sharing studies in Malaysia as a greenhouse-friendly and sustainable transportation mode.

Demand for cargo airships: An analysis of mode choice decision making in the freight transport industry

Despite their potential, cargo airships currently remain a theoretical concept. This research aims to examine the factors determining the demand for their services from the perspective of transport logistics professionals. An online freight mode choice experiment is conducted, supplemented with follow-up semi-structured interviews. Across freight types the sensitivity of the modes (road, rail, sea and airship) to the attributes (price, time, reliability and frequency) varied substantially. Willingness to pay for the cargo airship mode for travel time saved is estimated to be $23/tonne/hour. It is conservatively estimated that the potential market share of cargo airships in the Australian domestic freight market varies across airship models with LMH-1 having the lowest market share of 8% while ARH50 has the largest potential market share at 25%.

Analysis of Emergency Evacuation Modal Choice Behavior with Spatial Indicators: Case Study in Xi’an, China

To reduce the impact of a natural or man-made disaster, an evacuation is often implemented to transfer the population in the potentially impacted area to a safe zone. Evacuation is an effective measure for dealing with emergency events. This paper presents a multinomial logit model for modal choice behavior in a short-notice emergency evacuation, which incorporates spatial indicators into the utility function. The study examined the determinants of evacuees’ modal choice for three evacuation distances and analyzed determinants impacting the mechanism of the modal choice decision process. The data collected in Xi’an was used to provide empirical evidence for the relationship between spatial indicators and modal choice behavior. The findings of this study will help emergency planners and policy-makers develop strategies for evacuation planning and will enable a better understanding of emergency modal choice behaviors.

Multi-period multi-echelon inventory transportation problem considering stakeholders behavioural tendencies

An inventory transportation problem of manufacturing organization focusing on several stakeholders such as manufacturers, distributors, wholesalers, retailers and customers is addressed in this paper. The research study considered multi-echelon, multi-product, multi-modal and multi-period scenario. The mathematical model in the form of mixed integer non-linear programming is formulated to minimize the total cost associated with transportation, inventory holding and operational activities. A mathematical formulation based heuristic approach, which comprises of four algorithms, is proposed for solving purpose. The proposed heuristic approach considers the behavioural tendencies of stakeholders pertaining to the selection of shipment routes, transportation mode choice decisions and amount of products to be shipped. Fifteen practical problem instances are solved by using the developed heuristic approach while considering the behavioural aspects of stakeholders. Insights obtained from results will be beneficial for manufacturing organizations in making informed decisions related to transportation planning considering stakeholder's behavioural tendencies.

An investigation into the factors influencing inter-urban freight mode choice decisions in the Southern African Development Community region

Background: Two recurring issues in freight research regard the determination of the decision-makers in terms of freight mode choice and the modal attributes that shippers consider when making such mode choice decisions. Objectives: As few studies have been conducted in freight transport research in the Southern African Development Community (SADC) region; this paper provides empirical results on two research questions that allows the understanding of the freight procurement landscape in SADC. Firstly, who the decision maker is in terms of freight mode choice, and secondly, which modal attributes are consider and in which order, when shippers make mode choice decisions. Method: An online survey was conducted with 86 shippers, freight forwarders and third-party logistics parties across the SADC region to address modal attributes in freight mode choice. The exploded logit model was developed to draw inference from the data. Results: The results confirm that freight mode choice decisions are mostly affected by the shipper, with the freight forwarder being typically employed as the advisor. In terms of modal attributes, the results of an exploded logit model revealed that the top five attributes in terms of importance are reliability with reference to arriving on time, transport cost, risk of damage, frequency of service and transit time. Conclusion: These results can inform freight studies, especially shipper behavioural studies, which require the enumeration of attributes that can lead to improved reliable studies on freight transport.

Smartphone and daily travel: How the use of smartphone applications affect travel decisions

This paper aims to explore the covariates that affect the use of smartphones for trip planning as well as the covariates of perceived impact of smartphone use on travel outcomes. Trip planning activities considered in the study include performing online tasks, deciding departure time, mode choice decision, deciding trip destination and communicating and coordinating trips by using smartphones. Travel outcomes include the number of new places visited, social gatherings attended and trips planned in groups. This study emphasizes the role of built-environment and attitudinal variables on smartphone use for travel. Following ordered response and binary choice modeling approach, this study identifies that age and attitudes (toward smartphone use and environment) play a significant role in using smartphones for trip planning as well as shaping travel outcomes. Millennials (16–34 years) are more likely to use smartphones for trip planning and perceive increase in travel outcomes compared to other age groups. In addition, tech-savvy attitude is positively associated with trip planning and travel outcomes. However, the influence of built-environment characteristics is comparatively low in relation to the other attributes considered in the analysis. The study also finds that those who highly use smartphone apps for social networking purposes are more likely to perceive increase in their travel outcomes.

SEM-PLS Analysis Approach in Identifying the Effective Residential Bikeway Design Factors

The study was conducted to investigate the impact of residential-bike environment influences cycling participation among resident. The underpinning theories used in the study were the Ecological Model of Active Living (EMAF) and the Theory of Routine Mode Choice Decision (TRMCD). A structured questionnaire online survey was employed by engaging two resident associations (RA) in City of Elmina that contributed, 242 respondents. The data was analysed using Smart PLS 3.2.6 to test a developed model through measurement model analysis and followed by a structural model analysis. This approach offers comprehensive, quantifiable and flexible causal-modelling capabilities in a multi-regression analysis for non-parametric data that run simultaneously. The measurement model analysis was archived the threshold values for all criteria such as reliability and validity test. The structural model analysis result showed that perception, preference and satisfaction have significant positive relationship with the bike-built environment directly. Besides, the bike-built environment and cycling participation among resident has significant positive relationship. The Coefficient Determination, R2 of the bike-built environment was substantial value with 33 percent while the cycling participation was 7.3 percent. The study confirmed that the residential-bike built environment has impact on the cycling participation among resident.

Effects of violent crime and vehicular crashes on active mode choice decisions in New York City

Substantial research has explored the effects of different variables on mode choice with the intent of understanding this behavior so that active modes can be encouraged. This study furthers that effort by investigating the impact of perceived danger from crime on the probability of choosing an active mode of transportation among travellers in New York City from 2009 to 2011. This study uses trip data from the New York Metropolitan Transportation Council Regional Household Travel Survey along with historical crime and vehicle collision data to estimate a random utility model. Traveller demographic information, travel cost and incidence of crime and vehicle collisions involving pedestrians or cyclists are used as explanatory variables in a mixed logit model. The finding implies that travellers could be encouraged to cycle by reducing crime levels, or by being provided an alternative route with less crime. Based on the model results it can be determined that travellers are willing to pay $0.66 for a 1000-point reduction in crime severity. An increase in crime of 1% has a much greater impact on bike ridership (2.11% reduction) than on walking (0.06% reduction). Removing crime completely would improve a traveler’s trip satisfaction by as much as $0.26 per trip. Compared to crime, collision rate has a much stronger impact on bike ridership, with an elasticity of −7.56 (3.6 times higher elasticity than crime).