Trip Choice Research Articles

Safety-in-Numbers for route choice of bicycle trips: A choice experiment approach for commuters

Safety-in-Numbers (SiN) implies that the risk of collision per road user is less when there are more road users. Although the available literature has confirmed the existence of SiN as an objective measure of safety, the effect on perceived safety, especially in the context of bicycle riders, has received much less attention. This study investigates the SiN effect on the perceived safety of bicycle riders that influences route choice behavior. A stated preference survey was performed in the South Delhi district of Delhi. The effect of attributes like posted speed limit, the volume of motorized traffic, bicycle infrastructure, and bicycle traffic/ crowding on route choice behavior was investigated. A binary logit model was developed to quantify the effect of these attributes on route choice. The results indicate that, in general, riders prefer routes with more bicycle traffic, hence validating SiN. But the effect does not always hold. For some riders, in the presence of dedicated bicycle infrastructure, when the perceived safety is higher, the presence of more bicycle traffic acts as crowding and demotivates riders to choose that route. The study also reveals that riders prefer routes with a low volume of motorized traffic and dedicated bicycle infrastructure. The outcomes suggest that a policy that encourages infrastructural development to provide lateral separation will encourage more people, hence increasing bicycle mode share as well as the perceived safety of riders.

Analysis of trip frequency choice of commute trips in the context of COVID-19 in India: A hybrid choice modelling approach with generalized ordered logit kernel

The paradigm shift in mobility and travel behaviour caused by the successive waves of the COVID-19 pandemic has been unreal. The long-term effects of the pandemic resulting from the fear of the spread of the virus against the belief in the remedial measures are to be understood from a behavioural perspective to strengthen the current transportation system against such impediments. The current study adds to the literature on COVID-19 pandemic by unravelling the long-term impacts of the pandemic on the trip frequency of commute trips. A dataset of 467 individuals from all over India is analysed to understand the factors impacting the willingness of the respondents to reduce their commute trips in a post-vaccinated scenario. An integrated choice and latent variable structure, with a generalized ordered logit kernel, was considered to incorporate the influence of psycho-attitudinal variables and socio-demographics on the willingness to reduce trip frequency among individuals. The results indicate a significant impact for variables including fear of the virus spread, age of individuals, and job satisfaction of working from home on the stated willingness towards trip reduction. The study concludes by presenting policy measures that target to overcome the effects of the pandemic and restore normalcy.

Comparative Analysis of Discrete Choice Approaches for Modeling Destination Choices of Urban Home-Based Trips to Work

Discrete choice models based on the Random Utility Maximization (RUM) approach are alternatives to traditional aggregated trip distribution models. However, the Random Regret Minimization (RRM) approach has emerged as an alternative to RUM for analyzing behavioral phenomena. This study compares the RUM and RRM approaches to model the destination choice of urban home-based trips to work, with a case study in the city of São Paulo, Brazil. The attributes of distance between traffic analysis zones (TAZs), total number of jobs, and number of jobs per type (industry and services) were considered, and the attributes referring to TAZs in commercial areas were tested in different models. We combined a method to estimate the optimal choice set size and a stratified sampling method to choose alternatives from available choices for a destination-choice problem with many alternatives. The RUM and RRM models produced similar results with respect to the final log-likelihood, [Formula: see text], Akaike information criterion, and Bayesian information criterion for the studied case. However, the RRM models required 52 min to be estimated on average, whereas estimation of the RUM models took only 31 s on average. Models derived from the RUM approach including the total number of jobs and distance produced higher accuracy, requiring less computational time to estimate the parameters.

Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran

This study examines the determinants of online and offline shopping trip choices and their implications for urban transportation, the environment, and the economy in Tehran, Iran. A questionnaire survey was conducted to collect data from 1000 active e-commerce users who successfully placed orders through both online and offline services in districts 2 and 5 of Tehran during the last 20 days of 2021. A deep neural network model was applied to predict the type of shopping trips based on 10 variables including age, gender, car ownership, delivery cost, and product price. The model’s performance was evaluated against four other algorithms: MLP, decision tree, LSTM, and KNN. The results demonstrated that the deep neural network model achieved the highest accuracy, with a rate of 95.73%. The most important factors affecting the choice of shopping trips were delivery cost, delivery time, and product price. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. It aims to help them design effective strategies to reduce transportation costs, lower pollutant emissions, alleviate urban traffic congestion, and enhance user satisfaction all while promoting sustainable development.

Modeling the COVID-19 Travel Choices in Colombia and India: A Hybrid Multiple Discrete-Continuous Nested Extreme Value Approach.

The COVID-19 pandemic has affected many daily activities, primarily as a result of the perceived contagion risk and government restrictions to mitigate the spread of the virus. To this end, drastic changes in the trip choices for commuting to work have been reported and studied, mostly through descriptive analysis. On the other hand, modeling-based research that can simultaneously understand both changes in mode choice and its frequency at an individual level has not been much used in existing studies. As such, this study aims to understand the changes in mode-choice preference and the frequency of trips, comparing pre-COVID with during-COVID scenarios, in two different countries of the Global South: Colombia and India. A hybrid multiple discrete-continuous nested extreme value model was implemented using the data obtained from online surveys in Colombia and India during the early COVID-19 period of March and April 2020. This study found that, in both countries, utility related to active modes (more used) and public transportation (less used) changed during the pandemic. In addition, this study highlights potential risks in likely unsustainable futures where there may be increased use of private vehicles such as cars and motorcycles, in both countries. It was also identified that perceptions toward government responses had a significant impact on the choices in Colombia, though this was not the case in India. These results may help decision makers focus on public policies to encourage sustainable transportation by avoiding the detrimental long-term behavioral changes resulting from the COVID-19 pandemic.

Pilot survey of passengers’ preferences in Mobility as a Service (MaaS) scenarios: a case study

Transport and mobility are evolving towards the concept of Mobility as a Service (MaaS). MaaS is an integrated mobility system that considers the mobility needs of users as a central element of the transport service. Therefore, the mere definition of centralized and advanced technological systems is not enough to ensure the success of a MaaS.This paper presents a pilot survey for capturing the main behavioural variables (users’ choices) of transport users to support the ex-ante statistical analysis of a MaaS and to design the survey for analysing users’ behaviour.The knowledge of users’ choices may be carried out through existing information sources and statistical analysis. In a first stage, considered in this paper, the direct estimation of users’ choices started from interviews of a pilot sample of users, that may be supported by aggregate data (e.g. passengers’ flows measured on some elements of the transport network).In the current scenario, the surveys concern the detection of trip choices of users belonging to the pilot sample, interviewed in a virtual context, among a set of available trip alternatives.The main objectives of this paper are the following: to build an updated state-of-the art of survey methods concerning transport users’ behaviour; to identify the main disaggregated and aggregated variables of the transport system to be detected for supporting the analysis of MaaS; to obtain empirical evidences about transport users’ behaviour in presence of MaaS from the pilot users interviewed.

High-speed railway passenger flow equilibrium among trains of common lines based on travel behavior analysis under dynamic pricing

ABSTRACT This paper aims to make contributions to balancing the passenger flow among high-speed railway trains of common lines as well as raise revenue via a behavioral-based optimal dynamic pricing. Based on an HSR trip choice survey, a set of Nested-logit (NL) models is built to analyze passengers’ preferences for different factors under various scenarios. The upper level belongs to the departure time period while the lower level belongs to specific trains with different levels of service. Given the conditions of fixed transportation capability, an optimization model is built to optimize the pricing among trains of common lines. The NL model results show that the time values of round trips are greater than that of one-way trips, and the time values of business trips are greater than that of leisure trips. Besides, the optimization model is verified to be capable of raising revenue, improving passenger flow equilibrium, and inducing extra passenger demand.

Transportation emissions during pandemic: duality of impacts

Through the lens of an electric bicycle (E-bike) sharing program, this paper shows how the impact of the pandemic on transportation emissions can be multifaceted by impacting both (i) modal usage and (ii) trip generation. The pandemic led to a decrease in the number of trips by travelers, but it also induced a modal shift away from shared mobility into personalized modes. As a result, estimates of transportation emissions can present conflicting figures. In this work, we collect data on the travel behavior of users of an E-bike sharing program in Madison, Wisconsin, before and during the pandemic. Observations on trip choices and modal shifts of users provide means to estimate their relative and overall impact on transportation emissions. Results suggest that the influence of the pandemic on transportation behavior and environmental emissions is convoluted. Post-pandemic environmental implications would depend on the extent of recovery between modal shares and trip generations.

Mode choice analysis of school trips using random forest technique

Mode choice analysis of school trips becomes important due to the fact that these trips contribute to the second largest share of peak hour traffic. This scenario is more relevant in India, which has almost 265 million students enrolled in different accredited urban and rural schools of India, from Class I to XII as per the UDISE report of 2019-20. Thus, it becomes necessary to understand what mode of transport will be mostly used for school trips in order to design an efficient transportation system. Modal attributes and socio-economic characteristics are mostly considered as explana-tory variables in travel mode choice models. Multinomial Logit (MNL) model is one of the classic models used in the development of mode choice models. These logistic regression models predict outcomes based on a set of independent variables. With the recent advances in machine learning, transportation problems are getting a wide arena of methods and solutions. Among them the method of ensemble learning is finding a prominent place in contemporary modelling. This study explores the potential of using ensembles of random decision trees in mode choice analysis by Random Forest Technique with a comparative analysis on conventional method. It was observed that Random Forest method outperforms MNL method in predicting the mode choice preference of students. The high accuracy of machine learning models is mainly due to its ability to consider complex nonlinear relationship between socio-economic attributes and travel mode choice. These models can learn and identify pattern characteristics extracted from sample data and form adaptive structures through computational process thereby offering insights into the relationships between variables that random utility models cannot recognize. This study considered activity -travel information, personal data and household characteristics of students as attributes for model development and observed that the age of the student and distance of school from home plays a significant role in deciding the mode choice of school trips.

Dynamical efficiency for multimodal time-varying transportation networks

Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system. We propose for both single and multi-layered networks a path-based measure, called dynamical efficiency, which computes the travel time differences under congested and free-flow conditions. The dynamical efficiency quantifies the reachability of a location embedded in the whole urban traffic condition, in lieu of a myopic description based on the average speed of single road segments. In this way, we are able to detect the formation of congestion seeds and visualize their evolution in time as well-defined clusters. Moreover, the extension to multilayer networks allows us to introduce a novel measure of centrality, which estimates the expected usage of inter-modal junctions between two different transportation means. Finally, we define the so-called dilemma factor in terms of number of alternatives that an interconnected transportation system offers to the travelers in exchange for a small increase in travel time. We find macroscopic relations between the percentage of extra-time, number of alternatives and level of congestion, useful to quantify the richness of trip choices that a city offers. As an illustrative example, we show how our methods work to study the real network of a megacity with probe traffic data.

The Neighborhood Effect on Keeping Non-Commuting Journeys within Compact and Sprawled Districts

The neighborhood effect on keeping non-commuting trips inside neighborhoods has not yet been investigated in developing countries. The modeling of non-commuting trips inside neighborhoods helps understand how to avoid unnecessary journeys by car into different parts of the city. This paper, therefore, attempts to clarify (1) the similarities and differences in the socioeconomic characteristics and the perceptions of people in sprawled and compact neighborhoods, (2) correlations between, on the one hand, the choice of destinations of non-commuting trips for shopping and entertainment activities and, on the other, the socioeconomic features, travel behavior, and perceptions of residents in the two large Pakistani cities of Lahore and Rawalpindi, (3) the similarities and differences in the determinants of non-commuting destinations inside neighborhoods in compact and sprawled districts. The paper develops four Binary Logistic (BL) regression models, with two models for each type of neighborhood. The findings show that trips to shopping areas inside compact districts are correlated with a sense of belonging to the neighborhood, frequency of public transport use, residential location, and mode choice of non-commuting trips to destinations both inside and outside the neighborhood. On the other hand, the number of non-commuting trips, mode choice for non-commuting trips outside the neighborhood, frequency of public transport use, the attractiveness of shops, and monthly income (please see the Note) are significant determinants for trips to the shopping area in sprawled districts. Age, gender, possession of a driver’s license, income, number of non-commuting trips, mode choice for non-commuting trips outside of the neighborhood, car ownership, and attractiveness of shops in a neighborhood are correlated with trips to entertainment locations inside the neighborhood in compact districts. Finally, the attractiveness of shops, quality of social and recreational facilities, a sense of belonging to a neighborhood, choice of residential location, gender, age, possession of a driver’s license, number of cars in the household, and income are determinants of trips to entertainment locations in sprawled districts. A chi-square test confirms the differences across gender, daily activity, monthly income, frequency of public transport use, residential location choice, and the quality of social and recreational facilities for sprawled and compact districts in Pakistan.

Determinants of multi-destination travel in Vietnam: a rational choice perspective

PurposeThis paper aims to examine the factors affecting travelers’ decision to select more than one destination in their trips. Drawn from the rational choice theory, this study posits that the selection of multi-destination tours, as a rational choice, is determined by the travelers’ perception of costs and benefits associated with that choice.Design/methodology/approachThe conceptual model was developed from both an exploratory research with in-depth interview and the literature. A logistic regression model linking four explanatory variables representing perceived benefits and costs of multi-destination option with travelers’ choice of multi-destination trips. The conceptual model is controlled for diversity of travel purposes; tour group size; type of travel arrangement and travelers’ demographic characteristics while the research context of multi-destination travel within Phu Yen – Dak Lak – Gia Lai – Binh Dinh helps control for the characteristics of the destination set (spatial proximity and traffic convenience). The conceptual model was tested based on data collected from 1,361 Vietnamese travelers who have ever visited at least one of the four provinces.FindingsThis study finds that perceived benefits in terms of diversifying tourism experiences and perceived costs of traveling in multiple places compared to taking single trips represent the most significant drivers for tourists’ selection of multi-destination tours. In addition, the diversity of travel purposes and tour group size have a positive and significant impact on the demand for traveling more than one destination in a trip. Moreover, the richer, older and married travelers have a significantly higher tendency to select multi-destination packages.Practical implicationsThe research findings provide important strategic implications for promoting multi-destination trips, thereby, maximizing economic benefits from tourism.Originality/valueWhile multi-destination travel patterns have been examined empirically in individual studies sporadically without a common theoretical background, this research makes some progress on this front by using the rational choice theory to connect the relative contributions of those factors to the travelers’ choice of multi-destination trips. Specifically, this study provides a new perspective in explaining the selection of multi-destination trips – from tourists’ perceptions about costs and benefits associated with that choice.

Trip and mode choice decisions to out-patient health care facilities: A longitudinal analysis

This paper analyzes the impact of health indicators on an individual's trip and mode choices to out-patient care facilities. The study's focus is an out-patient trip to a health care facility, and the source of data is the China Health and Retirement Longitudinal Study (CHARLS) for 2011, 2013 and 2015. Based on a random utility framework, the study finds that making a trip to treat an illness or for a check-up increases the likelihood of an out-patient trip by 52 and 31 probability points, respectively. Out-patient visits for which insurance is not relevant, when the individual pays most of the out-of-pocket costs and when the facility is a public facility are also important factors. Diagnosed and other personal health factors have some but much more modest effects on one's trip choice. The analysis also identifies differential modal impacts of health indicators. A series of robustness tests generally confirm the results and identify areas for further research. Including a no-trip option, the biannual survey and infrequent out-patient trip-making mitigate endogeneity concerns. The analysis has broad health policy and transportation implications for an ageing population whose share is increasing.

Effect of Price and Time on Private and Shared Transportation Network Company Trips

Transportation network companies (TNCs) offer two types of service: private-party ridehailing and shared ridehailing. Policymakers have an interest in encouraging shared over private ridehailing to promote more efficient use of the transportation network. While transportation researchers have analyzed ridehailing behavior before, there is limited literature describing the effect of price and time on a rider’s choice between private-party and shared ridehailing. This paper fills this gap by analyzing revealed preferences for private-party and shared ridehailing trips in 15 American cities coupled with a survey of 4,365 users of a large TNC that includes stated preference questions focused on various alternative options for their most recent trip choice. This study finds that an increase in the relative price difference of $1 per mile increases an individual’s probability of sharing by over 8%, while a decrease in the relative travel time difference of 1 min per mile increases the probability of sharing by over 33%. The survey results also show that that a sizable proportion of private-party TNC trips (approximately 35%) will be difficult or even impossible to convert to shared rides through a price-based incentive. Market segmentation analysis reveals user and trip types where price- and time-based incentives have a relatively greater effect on the choice between private and shared rides. Finally, heterogeneity in user time versus money trade-offs suggests new product possibilities that would increase TNC sharing.

Integrating Passenger Incentives to Optimize Routing for Demand-Responsive Customized Bus Systems

The customized bus (CB) is an alternative public transportation mode that extends the flexibility and coverage of the fixed-route transit networks. It allows passengers to make reservations for trips and arranges vehicles to serve shared rides. However, the operation performance is limited to vehicle routing, the number of stops, and the length of detour times. Extra detours and stops would not be significantly avoided if deploying high-capacity vehicles. The demand control aspect is a possible way out. Releasing incentives to passengers can attract them to aggregated locations and reduce the vehicle detour times. How to determine an appropriate incentive scheme for passengers is the critical problem. This paper presents an approach to integrating the disaggregated trip choice model with the vehicle routing model to determine incentive schemes. First, the discrete choice model is established to bridge the passengers' trip choice probabilities with the influence of monetary incentives, walking time, and travel time. A vehicle routing model based on the pickups and deliveries problem is then adopted to generate the routes and schedules of vehicles to serve the influenced passengers. The result shows that the proposed approach can reduce the total running kilometers, shorten the onboard time, and increase the profits. The analysis also suggests that passengers' sensitivity towards incentives is decisive to the result.

Exploring the impacts of COVID-19 on travel behavior and mode preferences

Various measures were recommended or imposed by the governments to control the spread of COVID-19. Travel behaviors are significantly influenced due to such measures. However, people have various travel needs ranging from grocery shopping to work. This study examines the changes that occurred in travel behavior due to the COVID-19 pandemic. Data were collected through an online questionnaire survey that included questions on trip purpose, mode choice, distance traveled, and frequency of trips before and during COVID-19. 1203 responses were collected from various countries around the world.Results explained that trip purpose, mode choice, distance traveled, and frequency of trips for the primary travel were significantly different before and during the pandemic. Further, the majority of trips were made for shopping during the pandemic. There was a significant shift from public transport to private transport and non-motorized modes. People placed a higher priority on the pandemic related concerns while choosing a mode during the pandemic as compared to the general concerns. Gender, car ownership, employment status, travel distance, the primary purpose of traveling, and pandemic-related underlying factors during COVID-19 were found to be significant predictors of mode choice during the pandemic.Outcomes of this study could be useful in transport planning and policymaking during pandemics based on the travel needs of people. In particular, government authorities could utilize such knowledge for planning smart and partial lockdowns. Service providers, e.g., taxi companies and retailers, could use such information to better plan their services and operations.

Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data

During the passing decade, taxi floating car data (FCD) has become an important tool to investigate urban trip choice behaviors and activities. The corresponding taxi exhaust reduction issue is also with rather significance for traffic emission mitigation in urban areas. By taking Shanghai as an empirical case, this paper analyzed the spatiotemporal characteristics of multimode travelers by combining the taxi FCD (from Qiangsheng Inc.), the metro smartcard data and the GPS trajectories of Mobike, one of the most popular shared bicycles in China, 2018). Binomial logit models (BNL) were proposed to estimate mode choices for both peak and off-peak periods by incorporating socio-economic, demographic, urban morphology, land use properties, and various trip-related variables. The choices between metro and taxi, Mobike and taxi were analyzed, respectively, with the corresponding influential factors identified. The results indicated that the percentage of residential and commercial land uses, the number of educational facilities have significant impacts on travel mode choice during peak hours, while the percentage of commercial land, the number of hospitals and bus lines are more prominent during off-peak periods. To quantify the emission reduction benefits, localized calculation of automobile exhaust was established according to the Vehicle Specific Power (VSP) based measurements obtained from the Portable Emission Measurement System (PEMS) experiments. Then, five corresponding emission mitigation schemes were proposed based on the model findings, and the cost-benefit of each countermeasure was further analyzed. Comparing with releasing the peak-hour crowdedness of metro stations, increasing Mobike supply, updating taxis into electric vehicles, and equipping taxis with catalytic converters, the scheme of removing non-motor vehicle restrictions was found with the shortest payback period and was consequently recommended as accordance with the proposal of urban eco and non-motorized transportation. Findings of this study is useful for transportation management in improving the mode share of metro and bicycles, thus to alleviate the congestion and auto emissions in urban areas.

Investigating college students’ choice of train trips for homecoming during the Spring Festival travel rush in China: results from a stated preference approach

ABSTRACT This study is to investigate college students’ choice of train trips for homecoming during the Spring Festival travel rush, which is triggered by two facts in China that the annual Spring Festival travel rush has been challenging the national transportation system and that the high-speed railway in China has made a great progress in the latest years. To this end, a stated choice experiment was designed, where four hypothetical train trips (two conventional and two high-speed train trips) consisted of a choice set. A survey based on this design was carried out, where 2768 valid observations were collected. Given the potential correlations within alternatives and the heterogeneity of respondents’ preferences, a latent class nested logit model was established and estimated, in which two classes were identified that one payed more attention to economy while the other cared more about services. The estimation results answered the questions that which determinants and to what extent of these determinants have influence on college students’ choice of homecoming train trips and who are more likely to choose high-speed train trips. In addition, this study also provides analysis of willingness to pay and market share changes.

Investigating the Strategies for Reducing Motorcycle Mode Choice in Urban Trips: Case Study of Tehran City

Motorcycles in Iran, especially in metropolises such as Tehran, are used extensively for various reasons such as low maintenance costs, high maneuverability, and the possibility of entering congestion priced zones without paying a toll. Nevertheless, motorcycles are involved in 25% of accidents and produce almost 30% of air and 50% of noise pollution in Tehran. Current research aims to investigate possible scenarios for reducing the use of motorcycles in Tehran’s traffic and transportation master plan strategies. After designing the scenarios, a stated preference method is used for gathering the required data from various groups of motorcycle riders in Tehran. More than 2,000 questionnaires were completed of which 1,766 were deemed acceptable for data entering and further analysis. Increasing the price of motorcycle maintenance and charging motorcycles to enter the congestion priced zone of Tehran (CPZT) were two main scenarios which were considered in this research. Multinomial and nested logit models were applied to analyze the trip choice behavior of motorcycle riders who had participated in the survey. Results indicated that strategies such as increasing motorcycle maintenance costs could be effective in reducing the use of motorcycles. For instance, increasing motorcycle maintenance costs by 4.7 times and imposing a 70,000 IRR toll price to enter the CP zone resulted in a 66% reduction in motorcycle mode choice by motorcycle riders.

Variation in foraging strategies over a large spatial scale reduces parent–offspring conflict in Manx shearwaters

Parental care can lead to a conflict of interest between parents and offspring. For central place foragers, conflict is expected to be particularly intensive in species that feed on relatively inaccessible, distant food resources. Some pelagic seabirds use distinct foraging strategies when provisioning young versus self-feeding: short trips near the colony versus long trips far away. Limited empirical evidence shows that the strategy used by parents depends on their own state and that of their young, suggesting that dynamic optimization may help reduce conflict. Tests of this hypothesis, however, are scarce. Using a combination of GPS tracking and nest monitoring, we examined whether foraging strategy choice by Manx shearwaters, Puffinus puffinus, is explained by the body condition of parents and offspring before trip departure, and whether choice affects condition upon return. When chick body condition was poor prior to departure, subsequent foraging trips by adults were significantly shorter and chick condition upon return improved. When chick condition was good prior to departure, the reverse happened. There was no evidence that adult condition affected subsequent trip choice, but adults returning from slow, long-duration trips were in comparatively better condition. Thus, although the trips that were good for offspring were different to those that were favourable for adults, trip choice was only dependent on chick condition, which may explain why there was no evidence for a trade-off between adult and chick condition during individual trips. Our results suggest that spatiotemporal variation in foraging strategies is driven by the conflicting needs of parents and offspring, but that the parents can reduce the conflict, resulting in no detectable trade-off under these conditions. This link between parental care and space use is likely to be widespread in central place foragers but remains largely unexplored in most systems.