Trip Length Research Articles

Enabling gender responsive low-carbon transport in India using SDG5 framework

<p>Gender and mobility are strongly related and address challenges of safety and accessibility for women. Studies worldwide highlight the need to create more gender-responsive transport systems that maximize women’s mobility, civic participation, and safety. Gender inequality in urban transport is evident in Indian cities in terms of women’s longer walking trip lengths, longer time spent in commute and hence time poverty, impoverished health, and compromised personal safety. Even the public transport systems, often perceived as more gender-equal, overlook women’s needs and mobility concerns such as affordability and scheduling. This article presents transportation challenges faced by women (recorded through primary surveys and focus-group discussions) and further investigates how low-carbon urban transport, as perceived under the Paris Agreement and Sustainable Development Goals (SDGs), can be delivered with gender-equitable outcomes. For a more holistic perspective, the assessment is grounded in two Indian cities, Surat (4.5 million population), a fast-growing industrial metropolis, and Udaipur (0.5 million population), a small but important tourist destination in the nation.</p>

Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons

Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.

Exploring the relationship between built environment and bike-sharing demand: Does the trip length matter?

ABSTRACT Over the past decade, bike-sharing has garnered significant attention in both research and practice. As a manpower-driven transportation mode, the usage of bikes seems more sensitive to trip length, since one could take a shared bike to a destination where is too far to walk, or choose it for simply replacing walking when going to a nearby place. This paper identifies a threshold of bike-sharing trip lengths from bike-sharing trace data, and employs the Semiparametric Geographically Weighted Poisson Regression model to investigate the relationship between built environment and bike-sharing demand with different lengths by considering the heterogeneity in the relationship. Results show that built environment has heterogeneous effects on the bike-sharing demand in urban areas, and the effects differ across groups with trip lengths. The findings contribute to understanding the relationships between built environment and bike-sharing demand, and providing support for the placements and dispatching of shared bikes.

Handheld Cellphone Use and Risky Driving in Adolescents

Handheld cellphone use while driving is associated with increased motor vehicle crash risk among adolescents. To examine the association of handheld cellphone use while driving with kinematic risky driving (KRD) events-hard braking and rapid acceleration-in adolescent drivers. Adolescents aged 16.50 to 17.99 years licensed 365 days or less in Pennsylvania were eligible to participate in this cross-sectional study. Enrollment occurred from July 29, 2021, to June 6, 2022. Participants downloaded a smartphone telematics cellphone app to record driving data for 60 days. Trip characteristics, including frequency, length, and duration; presence of speeding; frequency and duration of handheld cellphone use; time of day; and presence of precipitation. Kinematic risky driving events per 100 miles driven. Zero-inflated Poisson regression models examined whether individual characteristics and trip characteristics were associated with KRD. Incidence rate ratios were computed. Of 405 adolescents who responded to recruitment, 151 enrolled, 140 completed study procedures, and 119 with 12 360 trips were included in the analytic sample (60 female participants [50.4%]; mean [SD] age, 17.2 [0.4] years). Adolescents drove a mean (SD) of 103.8 (65.7) trips, 565.0 (487.3) miles, and 25.1 (19.3) hours. Adolescents had minimal night trips (1.5% [192]), and few trips with precipitation present (9.0% [1097]). Speeding occurred in 43.9% (5428) of the trips and handheld cellphone use occurred in 34.1% (4214) of the trips. Kinematic risky driving events occurred in 10.9% (1358) of the trips at a rate of 2.65 per 100 miles. In adjusted models, increased KRD events were associated with handheld cellphone use (incidence rate ratio [IRR], 2.62; 95% CI, 1.53-4.48), speeding (IRR, 2.12; 95% CI, 1.06-4.26), and minutes driving (IRR, 1.02; 95% CI, 1.01-1.02). Trips at night, precipitation presence, licensure for less than 6 months, and sex were not associated with increased KRD events. In this cross-sectional study of adolescent drivers, trips with handheld cellphone use and speeding were associated with higher rates of KRD, while individual characteristics were not. The findings suggest that smartphone telematics apps provide an opportunity to observe behaviors as well as surveil changes due to intervention efforts.

Ketamine modulates the exploratory dynamics and homebase-related behaviors of adult zebrafish

Anxiety is a protective behavior when animals face aversive conditions, but commonly associated with various neuropsychiatric disorders when exacerbated. Drug repurposing has emerged as a valuable strategy to use existing pharmaceuticals for new therapeutic purposes. Ketamine, traditionally used as an anesthetic, acts as a non-competitive antagonist of the N-methyl-d-aspartate (NMDA) receptor, and evidence has shown potential anxiolytic and antidepressant effects at subanesthetic doses. However, the influence of ketamine on multiple behavioral domains in vertebrates is not completely understood. Here, we evaluated the potential modulatory effect of ketamine on the spatio-temporal exploratory dynamics and homebase-related behaviors in adult zebrafish using the open field test (OFT). Animals were exposed to subanesthetic concentrations of ketamine (0, 2, 20, and 40 mg/L) for 20 min and locomotion- exploration- and homebase-related behaviors were assessed in a single 30-min trial. Our data revealed that ketamine (20 and 40 mg/L) induced hyperlocomotion, as verified by the increased total distance traveled. All concentrations tested elicited circling behavior, a stereotyped-like response which gradually reduced across the periods of test. We also observed modulatory effects of ketamine on the spatio-temporal exploratory pattern, in which the reduced thigmotaxis and homebase activity, associated with the increased average length of trips were suggestive of anxiolytic-like effects. Collectively, our novel findings support the modulatory effects of ketamine on the spatio-temporal exploratory activity, as well as the utility of homebase-related measurements to evaluate the behavioral dynamics of zebrafish.

Physical activity through place attachment: Understanding perceptions of children and adolescents on urban places by using photovoice and walking interviews

Public urban places and their environmental characteristics impact youth's physical activity (PA) through perceptions. The objective of this study was to use a qualitative participatory approach with children and adolescents to understand how their attachment to urban places perceived as PA-friendly or unfriendly is related to their PA behaviour. Ninety-three participants aged six to 17 from six neighbourhoods with varying objective walkability engaged in photovoice and walking interviews. Data were analysed by using the tripartite framework of place attachment (PPP model), which was adapted for application to PA behaviour and supplemented by photographs. Themes were identified for each (sub-)dimension of the PPP model with person, place and process factors influencing attachment. Further subdimensions (PA and other behaviours) and categories (travel mode, trip length and frequency of visits) were added to the PPP model. Urban design recommendations were derived by age and gender to promote PA through place attachment.

A framework for modal split and implications on transport growth and travel time savings

Mobility is one of the most debated issues in the current discussion on transport policies and the climate change. Related indicators are the modal split, transport growth and travel time savings, used for travel behaviour evaluations. In official transport statistics, the modal split is generally used in two ways, according to the number of modal trips and to the modal trip length. Although they are usually used separately, we will show that they are directly connected, where the latter is a weighted version of the former. The introduction of the reference units of “person” and “day” overcomes the limitations of a trip-based definition and extends the ansatz to a mobility-based methodology, where all modal combinations can be considered. Empirical data analysis shows, that transport growth is founded on the increase of individual motorised transport at the disadvantage of pedestrians, whereas other modes remain fairly stable. Daily travel time generally increases especially with individual motorised transport in comparison to non-motorised modes, which shows an inconsistency, for example, for some assumptions or approaches of travel time savings. The proposed methodology does not require any prior assumptions and provides therefore a simple and general applicability. The data analysis is based on national travel survey data from Germany, Switzerland, UK and USA from the 1970-ies up to 2018. One main result of the paper is a frame of reference for the relativity of mobility behaviour, which consistently connects these mobility indicators.

Who is ready to live a car-independent lifestyle? A latent class cluster analysis of attitudes towards car ownership and usage

An excessive use of private cars for transportation has multiple negative effects on our society. It is therefore paramount to identify the underlying factors driving car usage among different segments of travellers. In this study, we aim to identify and characterise traveller segments in terms of their car-related attitudes and how different sociodemographic attributes, travel behaviour characteristics (such as using cars as the primary mode of transportation), and the accessibility of their place of residence vary amongst different population segments. Through Confirmatory Factor Analysis and Latent Profile Analysis we identify five different classes, namely car-detractors, −hesitants, −positives, −friends, and −lovers. More favourable car attitudes are associated with higher car ownership and access but, interestingly, there are no significant differences in terms of travel frequency and trip length distribution. This suggests that attitudinal differences relate mostly to modal preference rather than to the underlying travel demand patterns and the associated activities. We also see that car-less individuals are more likely to be car detractors, suggesting that the experiencing barriers for owning a car may contribute to developing more negative attitudes towards cars. Our results shed light on the different influencing factors and characteristics of individuals that consider, or not, a car-centred mobility.

Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway

Georeferenced messages on social media represent a powerful data source to gain a different perspective for estimating mobility behaviour, which is still mainly based on travel surveys. These data are openly available, yet few studies have explored their potential. This paper assesses the feasibility of large-scale Twitter data as a proxy of human mobility behaviour to complement traditional travel surveys, and for calibration and validation of transport models. Almost 12 million Tweets from more than 90,000 users were further analysed to detect the trip patterns at municipality level in Norway from 2012 to 2022. Results showed that the mobility patterns changed between 2014 and 2019 for the travel survey, as for 2019 most of the reported trips were short and concentrated in the densely populated areas of the country, where most respondents lived, triggering a lack of information for certain areas. In contrast, Twitter data presented a more stable data source along both years with similar population distribution and average trip length. Although Twitter data have limitations in relation to the socio-demographic information of the users, it could complement the travel survey given the broader spatial and temporal distribution of this large-scale data.

Evaluating the influence of cyclists’ route choices incorporation into travel demand modelling: A case study in greater Helsinki

Cycling is a sustainable transport mode that endorses an active lifestyle. While cycling shows great potential, it is essential for urban planning to consider attributes influencing the choices that cyclists act upon. Cyclists’ route choices have been studied since the Eighties with knowledge being applied in cycling network planning. Yet, the role of cycling as a sustainable transportation mode has been largely absent from travel demand modelling. This paper researches cyclists’ route choice preferences and evaluates the opportunity of incorporating route choice modelling into travel demand modelling to improve the accuracy of cycling route choice. To this end, a route choice framework is developed in which a stated preference survey for data collection is conducted, a multinomial Logit model is applied to the data to identify the factors that significantly influence cyclists’ route choice behaviour. The generated route choice utility models are further integrated into an existing regional travel demand model to evaluate the performance of cyclists’ route choice modelling in the presence of additional factors. Then, the route choice model outputs are validated against two sets of external data. The results show that bike facilities, traffic volume, and trip length are the key factors influencing cyclists’ route choice preferences, and the generated route choice models can be an applicable improvement in incorporating the influences of cyclists’ preferences into travel demand modelling.

Northern Gannet foraging trip length increases with colony size and decreases with latitude

Density-dependent competition for food influences the foraging behaviour and demography of colonial animals, but how this influence varies across a species’ latitudinal range is poorly understood. Here we used satellite tracking from 21 Northern Gannet Morus bassanus colonies (39% of colonies worldwide, supporting 73% of the global population) during chick-rearing to test how foraging trip characteristics (distance and duration) covary with colony size (138–60 953 breeding pairs) and latitude across 89% of their latitudinal range (46.81–71.23° N). Tracking data for 1118 individuals showed that foraging trip duration and maximum distance both increased with square-root colony size. Foraging effort also varied between years for the same colony, consistent with a link to environmental variability. Trip duration and maximum distance also decreased with latitude, after controlling for colony size. Our results are consistent with density-dependent reduction in prey availability influencing colony size and reveal reduced competition at the poleward range margin. This provides a mechanism for rapid population growth at northern colonies and, therefore, a poleward shift in response to environmental change. Further work is required to understand when and how colonial animals deplete nearby prey, along with the positive and negative effects of social foraging behaviour.

Measuring heterogeneity in pedestrians’ perceived importance towards crossing facilities: An experience in Roorkee

Although several studies in the past discussed the effects of socio-demographic and trip characteristics on users’ preferences towards crossing facilities in urban areas, they were mostly confined to metro cities or million-plus cities. Adequate investigation has not been performed for the same in the small-sized cities where pedestrian activities are expected to be more frequent because of the higher share of shorter and walkable trip lengths. Accordingly, the present study aimed to investigate the heterogeneity in the perceived importance of crossing facility attributes among different pedestrian groups based on gender, age group, and walking preference. The study was conducted in a small-sized city, Roorkee, in the state of Uttarakhand, India. A face-to-face questionnaire survey of 554 pedestrians was conducted to collect their perceived importance towards 12 crossing facility attributes under study using a 5-point Likert-type ordinal scale. Collected data was analyzed using the non-parametric tests to measure the heterogeneity in the perceived importance of crossing facility attributes among aforementioned pedestrian groups. Subsequently, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to derive relative importance scores of attributes found statistically significant in heterogeneity tests for different pedestrian groups. The findings of the present study reveal that perceived importance towards crossing facility attributes is heterogeneous based on the pedestrians’ age group and walking preferences; however, it remains unaffected by gender. These findings would help policymakers, planners, and government authorities frame improvement strategies for the existing crossing facilities in the city.

Ecological and economic predictors of métiers in a mixed fishery

Abstract Marine ecosystem-based management requires the understanding of species interactions and what species are harvested together. This study combines two major questions: the first regarding what drives the probability that a métier (species assemblages, with spatial distribution and seasonality) will be observed as catch, and the second regarding the level of control fishers have over this catch mix. To address these questions, we analysed highly resolved logbook records of an Arctic and sub-Arctic industrial demersal fishery operating in Icelandic waters. The study employs a multi-class random forest model to identify predictors of métier occurrence and consistency of predictions using a dataset of >100 000 hauls over 4 years (2016–2019). The overall accuracy of the random forest model is 69–70%, indicating moderate predictability of catch mix based on known environmental, vessel, and company characteristics. We find that habitat-related variables (depth and temperature) are most important to predict catch mix. Still, company, trip, and vessel characteristics are also very important (e.g. vessel and trip length, distance to port). Beyond these more traditional bio-economic variables, important predictors include variables related to harvesting strategies, such as quota diversity and a vessel’s mobility. These findings contribute to a fuller picture of fisher decision-making in mixed fisheries.

Distributions of walking access to public transport in Melbourne, Australia – Evidence on acceptable and tolerable walking distances

The international literature indicates a wide interest in the distances public transport users walk to access their services. Urban and transport planners seek information on acceptable walking distances (AWD) in the provision of minimum levels of service coverage. This study uses a large database from Melbourne, Australia, to analyze trip length frequency distributions (TLFD) of walking access and egress to consider AWD in a multimodal public transportation system and to examine tolerable walking distances (TWD). AWD provides a guide to planners about stop/station locations for desirable minimum service coverage. TWD is a representative maximum walking distance for assessing the effectiveness of that service provision and can be used in conjunction with AWD. A statistical distribution function for walking distances can facilitate the use of regional values for AWD and TWD in transit service planning. The Burr Type XII distribution is shown to provide a good fit to the observed data. This provides a valuable tool for the analysis of percentile walking distances and suggests a general framework for the study of AWD and TWD in any city or region.

Acceptance and demand of autonomous vehicles for long-distance recreational travel: An investigation based on a survey of visitors to US national parks

The study of anticipated changes in travel behavior that could be brought by autonomous vehicles (AVs) has been widespread for commuting and short-distance daily travel, yet little attention has been given to long-distance travel. To address this research gap, this paper presents an analytical model for estimating public preference towards the adoption and use (in terms of frequency and length of trips) of AVs, focusing on long-distance recreational travel (LDRT). The model considers socio-demographics, trip-specific characteristics, in-vehicle time use-related factors, and attitudinal latent variables (AV usefulness, AV concern, driving enjoyment) as potential influencers of AV acceptance and demand. To analyze the proposed model, data were collected from a survey of visitors to US national parks conducted in Summer 2022, and the structural equation modeling (SEM) technique was employed. The results indicate that the frequency and length of long-distance recreational trips will likely be higher in the AV era. This draws attention to tourism destination managers not only to manage the tourists’ demand at destinations but also to manage traffic on the roads leading to the destinations. Additionally, the results show that the LDRT demand will continue to rise with the increase in AV acceptance due to the usefulness and potential for several in-vehicle activities offered by vehicle automation, despite concerns about system safety, data privacy, and legal liability. The study also reveals that some travelers will likely miss manual driving enjoyment in AV driving, particularly in unique travel settings like tourism travel, and thus might opt for manual driving options. Based on these findings, we advocate for the timely consideration of induced LDRT, and tourism travel demand generated by AVs—such as provisioning sustainable public transport options to recreational and tourism destinations.

Even a journey can be a destination: Exploring the spatial patterns of motorcycle traffic in the Czech Republic based on official and crowdsourced data

Not only in geography is motorcycle transport still a very under-researched phenomenon. Although there has been a recent growth in academic interest in motorcycle transport, there are still some key questions that remain unanswered. Empirical studies analysing motorcycle transport are surprisingly also based mainly on observations in the Global South, with very little research in the economically developed countries of the Global North. This paper aims to examine the spatial patterns of motorcycle transport in the Czech Republic. For the analysis, we use official data on average motorcycle intensities on the road network (i), and unofficial data crowdsourced from individual GPS navigations and social networks (ii). We found major inconsistencies between the two data sources. The paper’s main contribution is to demonstrate the great potential of crowdsourced data to identify important aspects of the spatial organisation of motorcycle traffic. Based on these data, we found substantial differences in the intensity of motorcycle traffic on the road network (different use of individual categories of communications according to both types of data), the popularity of individual motorcycle routes (higher popularity of scenic roads in GPS data), and the different lengths of individual trips during weekdays and weekends.

Meeting the charging demand of Electric Vehicles in Greece: Enabling intercity trips

The increasing need for sustainable transportation has underscored the pivotal role of electric mobility in shaping the future of mobility. Electric Vehicles (EVs) have emerged as a promising solution, but their widespread adoption is contingent upon the availability of a reliable charging network. This challenge extends even to EV adoption pioneers like Norway and China. This study delves into the context of EV adoption in Greece, providing insights into the EV market, existing incentives, and the state of charging infrastructure. Reports indicate that Greece is at a critical juncture, with fast EV adoption but sluggish development of Charging Stations (CSs). To address this issue, a Monte Carlo simulation is employed to evaluate the feasibility of long-distance EV trips in Greece and propose an infrastructure development plan. For EVs, the study reveals a linear relationship between battery size and autonomy, with some exceptions emphasizing the importance of data acquisition and individual EV assessment. The study also provides insights into the relationship between trip length and energy consumption, indicating that longer trips exhibit greater fluctuation in energy consumption due to varying conditions, while energy usage patterns of shorter trips are more predictable. Additionally, the analysis presents average energy consumption values for each EV model in different temperature conditions, highlighting the impact of environmental factors on EV performance.

Impact of autonomous vehicles on discretionary activities: an agent-based model with space–time accessibility constraints

AbstractThe extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.

Modelling cycling to school in Finland

Active travel to school is an important contributor to the physical activity levels of children and adolescents and the source of many health benefits. In this work, we model cycling to school in Finland. The probability to cycle on a given trip is modelled using a binary logistic regression model based on trip length, average route gradient, the cyclist’s gender, and age. Variables denoting the city regions were also included to account for differences in cycling cultures and infrastructure. In addition, weather variables were added as cycling levels in Finland are highly dependent on the season. Air temperature and the presence of snow were found to reflect well the observed seasonal variations. The observed influence of winter conditions on trip distances and the cycling of girls is also replicated in the model through interaction terms. This model is employed to explore two sustainable mobility scenarios: a scenario where the cycling of girls increases to the same level as that of boys and a scenario where all school children cycle as much as those living in Oulu, Finland’s top cycling region. Our results suggest that it would be possible to increase the number of trips by bicycle and cycled mileage significantly, up to 76 % in the Oulu scenario, even though school children already cycle much more than the general population.

Shopping trip recommendations: A novel deep learning-enhanced global planning approach

Brick-and-mortar shopping malls are embracing Artificial Intelligence (AI) technology and recommender systems to enhance the shopping experience and boost mall revenue. Echoing this trend, we formulate a new shopping trip recommendation problem, which aims to recommend a shopping trip (i.e., a list of stores) that matches customer preferences and has appropriate trip lengths. To solve this problem, we develop a novel deep learning-enhanced global planning (DeepGP) approach featuring three methodological novelties. First, we introduce a new shopping intensity term based on deep neural networks to capture the variation of trip lengths specific to different shopping contexts. Second, we innovatively formulate the learning and optimization objectives in a consistent form by balancing the shopping choice likelihood and the shopping intensity likelihood, thus resolving the inconsistency issue encountered by prior global planning methods. Third, to overcome the computational challenge caused by the nonlinear shopping intensity term, we design a new exact and efficient solution technique based on piecewise linear transformations. Using a real-world offline shopping dataset, we empirically demonstrate the superior performances of our approach compared to representative benchmarks in offering more accurate and relevant shopping trip recommendations. Through a simulation, we show the capacity of our approach to attract and balance customer traffic in practical deployments. Overall, our research highlights the efficacy of combining shopping choices and shopping intensity in a consistent learning and optimization framework for offline shopping trip recommendations.