Short-distance Trips Research Articles

A copula-based approach for multi-modal demand dependence modeling: Temporal correlation between demand of subway and bike-sharing

As a representative mode of shared mobility, bike-sharing serves not only as a convenient way to conduct short-distance trips in urban areas, but also as a feeder mode to public transit, forming the Bike and Ride (BnR) system. Conducting management for such a hybrid multi-modal system faces various challenges, including the complex interactions between bike-sharing and other modes, highly dynamic passenger demand, and the difficulty of accessing direct transfer data. To overcome such difficulties, our study proposes a framework for assessing the dependency between the two usage modes. Firstly, a Dynamic-Time-Warping-based (DTW) method is utilized to determine the catchment area (CA) between the two modes, allowing the BnR-related tendency similarity under a given time scale to be considered. Then, the patterns of probabilistic dependence between travel demand of the two modes are obtained by a copula-based approach, which separates correlations under specific usage levels from single modal demands. A case study on the multi-modal system formed by docked bike-sharing and subway in New York is conducted to validate the proposed framework. The tendency similarity is found to be most pronounced within 500 m on average under a 4-hour interval. For each formed station group (SG), the best-fitted copula type is selected, capturing the strong tail correlations present only at specific usage levels. The results show a variety of different correlation patterns within SGs, despite the close geographic locations they may share. Areas of potential transfer resistance between the two modes are identified, which is more evident in first-mile-related (FMR) activities. In contrast, the two modes display more weak connections in last-mile-related (LMR) activities. The obtained results can be utilized by bike-sharing service providers to analyze demand distributions and conduct efficient station-level rebalancing. Compared to previous methods, our proposed framework is computationally inexpensive since no direct transfer of data or complex inference network is required. It incorporates statistically significant spatial–temporal information, allowing for a more accurate determination of the bi-modal assessment range. Moreover, considering that single-mode influences are mathematically removed, the resulting correlation in principle links to the strength of the connections between the two modes. Therefore, it can be assessed as an indicator of the reliability of the multi-modal system.

Quantifying spatial interaction centrality in urban population mobility: A mobility feature- and network topology-based locational measure

Spatial interaction centrality reflects the relative importance of population mobility within a location in urban population mobility. Population mobility networks visually represent urban population mobility, with mobility features and network topology contributing to the quantification of spatial interaction centrality of locations (i.e., geographical nodes). However, existing centrality measures rarely consider mobility features and network topology simultaneously. Centrality quantification also ignores the differences in distance effects between long- and short-distance trips. These factors have led to the inaccurate quantification of centrality. We propose an algorithm called k-dis-weight-shell that quantifies the spatial interaction centrality of geographical nodes at different spatiotemporal scales. Considering the different effects of distance on long- and short-distance trips, we use a spatial continuous wavelet transformation to estimate the radiation radius of geographical nodes. Then, by combining network topology with mobility features (mobility distance and flow), the algorithm transforms them into a ranked order of spatial interaction centrality. Tested in Wuhan and Chengdu, our algorithm outperforms six existing benchmarks. For cases in urban planning and epidemic management, results show that k-dis-weight-shell effectively distinguishes similarities and differences between the distribution of population mobility's spatial interaction centrality and the urban center hierarchy at a coarse spatiotemporal scale. Additionally, it reveals a double wave phenomenon of spatiotemporal correlation between population mobility and COVID-19 transmission before and after lockdown at a fine spatiotemporal scale.

Choosing to drive from alcohol serving establishments (ASEs).

The prevalence of Driving Under the Influence (DUI) of alcohol or drugs has become a prominent factor in the occurrence of severe road crashes worldwide. Driving often occurs after visiting, and presumably drinking, at Alcohol-Serving Establishments (ASEs), and is thus of interest as a possible source of DUI events. We apply statistical and machine learning models to the Victorian Integrated Survey of Travel and Activity (VISTA) to identify factors that contribute to driving in trips from ASEs in Australia's state of Victoria. Our results highlight that approximately 10% of individuals who traveled to ASEs as car passengers switched to driving after leaving there. It was also observed that travel distance shorter than 1 km and activity duration between 3 and 4 h positively impacts the mode switching from car driver to other modes in ASEs trips. Further findings illustrate a decline in driving after midnight, with an increase in the use of public transport and taxis. Individuals prefer driving for long-distance ASEs trips and walking for short distances. Going home also increased the likelihood of driving, whereas engaging in other social activities did not. Longer stays at ASEs and leaving vehicles overnight reduce the propensity to drive, likely due to increased alcohol consumption during these times. These findings suggest behavioral adjustments that can mitigate driving under the influence. Specifically, people may walk for short-distance trips and use public transport or taxis for longer ASEs trips.

Energy and Carbon Savings in European Households Resulting from Behavioral Changes

The study evaluates the impact of behavioral changes resulting from climate awareness on energy consumption and carbon emissions in European households based on the results of a two-stage survey addressed to individuals manifesting pro-ecological attitudes. In the first stage, the author analyzed 67 pro-environmental behaviors declared by the participants, identified a set of new sustainable choices, and compared them to the conservation habits used in Rasch and Campbell’s models. The 10 most popular initiatives undertaken by over 50% of participants were selected for further analysis. The influence of these initiatives on energy consumption and CO2 emissions was assessed. A total of 24 impact indicators were identified at the building scale. Energy and carbon savings were calculated for 500 participants from 28 European countries and compared to the results computed for the 100 households used as a reference. The main conclusions from the research concern the significance of individual decisions at the building scale in the context of their actual environmental impact calculated for a larger scale. The comparative analysis showed that the highest annual energy (2292.1 MWh) and emission (267.02 tons of CO2) savings resulted from the car-to-bicycle (or walking) transition on short-distance trips (declared by 79%) and from the transition from non-renewable to renewable energy sources (PV panels installed by 65% of respondents). Annual energy and emission savings reached, respectively, 1300 MWh and 262.6 tons of CO2. The research findings help explain the critical importance of transforming the built environment towards renewable energy sources and supporting pedestrian and sustainable transportation.

Do two-wheeler riders propel towards autonomous driving? A path-based probabilistic study in Indian context

Regardless of the rational benefits of autonomous vehicle adoption towards mitigating mobility goals in urban and sub-urban areas of middle-income countries, adoption of such technology remains thought-provoking. Two wheeled vehicles are often utilized extensively all over the world for short distance trips in sub-urban areas. The present study investigates perceived risks of using four wheeled autonomous vehicles by captive two-wheeler riders for long and short distance trips in densely populated Indian cities. Theoretical autonomous vehicle technology acceptance models were modified considering Indian perceptions in this study. Offline stated preference responses were collected from various universities, open public spaces and governmental organizations of various cities within India. Structural equation models (SEM) and Random Forest (RF) were employed to generate meaningful insights from the surveys conducted. Random Forest (RF) analysis concluded that bicycle riders show medium to low levels of trust towards behavioural adoption intention of autonomous vehicles. According to proposed structural equation models, interest to adopt or willingness to buy autonomous vehicles for two wheeled vehicle users is mostly influenced by perceived trust and risk rather than perceived concerns/benefits for middle and old age groups in India. Perceived ease of use has the most significant marginal effects on two-wheeler riders' perceived risks towards autonomous vehicle adoption, which accounted for more than 20 % of unexplained variance. Results from this research may offer insightful diversity of attitudes towards adoption of autonomous vehicle technologies in India through the means of increased efficiency and reduced travel time and costs along with riders’ risks towards privacy and safety.

Shared low-speed autonomous vehicles for short-distance trips: agent-based modeling with mode choice analysis

ABSTRACT Previous research has envisioned behavior changes with the availability of shared autonomous vehicle (SAV) services. It is expected that SAVs will compete with conventional human-driven vehicles and lead to a shift in mode share. If designed as single-occupancy, low-speed, and lightweight, SAVs can also be considered a micro-mobility mode targeting short-distance trips in geofencing areas, which has been lacking in discussion in the state-of-the-art literature. To address this gap, this study envisions changes in mode choice with the implementation of a single-occupancy Shared Low-speed Light-weight Autonomous Mobility (SLLAM) system deployed, serving trips at speeds below 25 mph. Agent-based simulations are conducted to evaluate the mode share of the SLLAM system. The findings demonstrate that the SLLAM system has the potential to serve 36.98% of the trip demand in the study area. Increased operating speeds enhance the SLLAM mode share, making it a more attractive option for existing private vehicle trips.

How does extreme temperature affect shared travel? Evidence from bike-sharing order flow in China

Due to its flexibility, pro-environmental characteristics, and its contribution to solving the first or last kilometer’ problem, sharing bike has become a popular travel option for many residents, especially for short trips. This study analyses the impact of extreme temperatures on residents' shared travel and the bike-sharing industry by combining the full-sample travel data, meteorological data, and POI data in Hohhot, China. Based on the temporal and spatial characteristics of residents' travel, five patterns are distinguished by K-means clustering: basic trips, short-distance trips, round trips, time tolerance trips and distance tolerance trips. The main conclusions of the research are as follows: i) extremely low temperatures can increase the possibility of short-distance trips and round trips but reduce long-duration or long-distance travel. Extremely high temperature has a positive effect on residents' round trips and time tolerance trips but suppress short-distance trips and distance tolerance trips; ii) for long-duration or long-distance shared travel, extremely low temperature enhance the connection between sharing bikes and other modes of transportation, but the extremely high temperature has the opposite impact; iii) extreme temperature causes less substitution of sharing bikes for other vehicles in round trips. However, residents in basic trip mode prefer to use sharing bikes instead of vehicles at extremely high temperatures; iv) the total annual economic loss of shared travel caused by extreme temperatures reached 31,393.82 CNY. In addition, many residents abandon sharing bikes in favor of cars at extreme temperatures, leading to additional carbon emissions of 2585.29 kg/year. The study also provides profound policy implications for the bike-sharing industry and green travel.

Exploring the prospects and challenges of sustainable urban mobility: Potential and limits of cycling in Venice

The Covid-19 pandemic has affected the travel behavior of commuters, with soft modes emerging as reliable options for short-distance trips. This research focuses on evaluating the bike-friendliness of Venice, Italy, a unique city for its morphological and mobility characteristics. When considering daily commuting between the mainland and the historic city center in the lagoon, the bicycle is not an adopted solution. Yet, the recent construction of a bicycle and pedestrian path that runs alongside the main bridge between the mainland and historical city could alleviate the pressure on public transport and the use of cars, especially in peak hours. This contribution evaluates the potential for using bicycles to reach the historic center of Venice from the mainland, and the appropriateness of the infrastructural equipment. The quantitative analysis examines the current supply and demand in absolute values and in terms of modal share. Projecting the number of actual users under different scenarios until 2030, in accordance with the Venice Sustainable Urban Mobility Plan and other relevant plans, the inadequate provision of parking areas for bikes emerges as an unsolved issue. A revision of the mobility layout is thus required if bicycles are expected to be a competitive alternative solution.

Ride-hailing Service and The Shape of Urban Mobility (Case Study: The City of Makassar, Indonesia)

The significant gap between supply and demand of flexible and comfortable public transport within cities in the lower-income countries encourages the opportunity for the private sector to provide a ride-hailing service. Since its introduction in 2010, the ride-hailing service has continued to grow and has shaped urban mobility in the past twenty years. Due to the growth of ride-hailing services, some concerns arise regarding their contribution to the increasing traffic volume and the increased dependency on smaller-capacity vehicles in citizen mobility. This study aims to: 1) analyze the travel patterns of ride-hailing users based on their mobility needs; 2) analyze the relationship between travel characteristics and users’ socio-economic background and travel characteristics; 3) analyze the implication of ride-hailing users in shaping Makassar’s urban mobility. This study focused on the city of Makassar as the case study. The data in this study was obtained from an online survey of ride-hailing users with 270 samples. Several approaches are used for the analysis, i.e., descriptive statistics, spatial analysis, and network analysis. The result shows that 1) Most of the ride-hailing users are young generation (Gen Z). They perform a short-distance trip daily (internal trip within the district). 2) Regardless of the users’ age, income, and private vehicle ownership, ease of service is the foremost preference of the users since it is application-based and can be accessed using a smartphone. Door-to-door service is also one of the advantages provided by ride-hailing services.

How shareable is your trip? A path-based analysis of ridesplitting trip shareability

As an emerging sustainable mobility solution, ridesplitting services match passengers in a similar direction with a single vehicle to reduce fleet size, vehicle kilometers traveled and traffic emissions. However, these benefits can only be achieved with successful matching (sharing) between passengers, which emphasizes the importance of a comprehensive understanding of the matching success rate, i.e., shareability. Despite extensive research into the determinants of shareability, existing literature either relies on simulations and theoretical models with limited empirical validation, or focuses on system-level shareability for the whole market, overlooking the significant spatiotemporal variability of shareability across trips. This study aims to fill these gaps by proposing a path-based model that leverages real-world ridesplitting data to quantify the determinants of shareability at a finer spatiotemporal granularity. Utilizing data from New York City, our results show that: (1) shareability is spatiotemporally heterogeneous; (2) high demand intensity, especially the intensity of medium−/short-distance trips, contributes to greater shareability; (3) the positive contribution of demand intensity diminishes as it increases; (4) a higher road speed improves shareability; (5) excessive one-way street and over-dense street network are related to low shareability. These findings validate and enrich prior findings, which can be used to inform the future development of ridesplitting services.

Developing a statewide active transportation planning dashboard with mobility data

ABSTRACT Existing walking/biking data are often insufficient to support active transportation planning activities. This study used an emerging large-scale human mobility dataset to identify places where there are a higher number of short-distance trips to public places (expressed by a mobility index). Demographic variables were integrated into the mobility index to prioritize access for more people and address equity concerns. In addition, a safety index (reflecting crash injuries/fatalities involving pedestrians/bicyclists) and a connectivity index (reflecting the density of existing active transportation infrastructure) were also considered together with the mobility index in generating an investment potential score. All the indices/scores were mapped at hexagon and segment level on an online dashboard. The developed dashboard is expected to support statewide active transportation planning and project selection/prioritization decision-makings. The proposed methodology is replicable for any places that have few active transportation facilities and where pedestrian/bicyclist count data are not sufficient to model demand directly.

Greenhouse Gas Emissions and Potential for Electrifying Transportation Network Companies in Toronto

This study investigates the spatial and temporal patterns of greenhouse gas (GHG) emissions from transportation network companies (TNCs) in Toronto, Canada. TNC services primarily consist of short-distance trips in the central business district. Deadheading—driving without passengers—contributes 40% of TNC GHG emissions, and part-time drivers had a higher deadheading proportion than full-time drivers. Pooled TNC trips account for 10% of total trips, and just 27% of pooled trips resulted in multiple passengers sharing a vehicle on the same route. The GHG implications of pooled trips were compared with other TNC services and with a consumer driving their own private vehicle, through the estimation of median trip emission intensities of each TNC service in grams of CO2eq per passenger-km, while accounting for deadheading. Non-pooled internal combustion engine (ICE) ride-hailing trips have a median emission intensity 61% higher than that of a single-occupancy private vehicle. The median emission intensity for a pooled ICE ride-hailing trip is 20% higher than that of driving one’s own vehicle. Vehicle electrification provides a 91% daily GHG emission reduction for this fleet. This reduction is mainly achieved through full-time drivers, who, on average, achieve three times as much GHG savings per electrified vehicle compared with part-time drivers.

Understanding the impact of laziness and pedestrian intervention on car dependence within the context of a European island

Sedentary behaviour, including sitting in motorised vehicles, has been linked with negative physical and psychological health repercussions, even in sufficiently physically active individuals. It is therefore pivotal to identify factors and potential determinants associated with this sedentary behaviour, including the role of the urban environment. Within the plethora of studies analysing habits and experiences as psychological determinants, few have delved into the concept of laziness and car use. This research explores the phenomenon of laziness by measuring people's perceived physical exertion to walk for short-distance trips and applies the ‘Lazy User Theory’ to examine the constructs of people's lazy user behaviour in modal choice. Using questionnaires, interviews and workshops, the paper seeks to understand the link between laziness and car use for short-distance trips by using pedestrian intervention as a tool for analysis. The findings show significant links between higher frequencies of car use and increased perceived exertion of walking for short-distance trips. Although the preliminary application of the Lazy User Theory model was not able to fully explain the behaviour of car-use for short-distance commuting trips, the construct of mental effort provided the best explanation for this behaviour within this context. The paper concludes with policy-relevant feedback from the community on pedestrian interventions to encourage a shift to walking and the next steps for further research into understanding laziness as a determinant of travel behaviour.

Optimal planning of urban air mobility systems accounting for ground access trips

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low.

Use of passive data for determining link level long distance trips

Long-distance trips include a high value of time compared to short-distance trips; thus, capturing long-distance trips contributes to substantial economic and social benefits. This study utilizes privacy-protected travel data collected from mobile devices in Maryland to identify the link-level proportion of long-distance vehicle trips. We propose applying existing econometric frameworks, i.e., the generalized linear and beta regression models, to predict these link-level long-distance trips. Among the covariates, we utilize highway network-level attributes from the Maryland statewide travel demand model (MSTM), employment, and other open-source datasets available for the state of Maryland. Both the econometric models were compared for their performance in multiple measures like validation, behavioral interpretation, and goodness of fit. The best model results indicate a positive relationship between the proportion of long-distance trips and link attributes, like functional class, speed, and surrounding household density. The proposed framework will provide useful insights and key inputs for practitioners in demand modeling in capturing long-distance travel in a roadway network.

Long-term urban railway network expansion and pollutant emissions: Methodological approach and insights for sustainable transportation policies

Urban transportation is a major challenge in many cities worldwide and railway systems are often considered as an alternative to solve mobility and air pollution problems. However, such systems are capital-intensive, and their implementation often extends over several years, particularly in developing countries. This paper proposes a methodological approach to assess the impact of long-term urban railway network expansion on pollutant emissions applied to the Metropolitan Region of São Paulo (MRSP) in Brazil. The proposed approach consists of seven steps: define the trips of interest in a database derived from an OD survey; estimate the trip matrix in a baseline scenario and a future scenario considering the expansion of the rail network; determine the trip attributes in both scenarios; calculate the modal choice based on a Logit model; determine the number of trips per mode; estimate vehicle emissions using the bottom-up methodology; and compare the emissions between scenarios. The results of the proposed railway network would lead to a substantial increase in railway ridership between the base scenario of 2017 and the future scenario of 2030, reducing the choices for automobiles by 41%. Nonetheless, the decrease in vehicular emissions would be modest, primarily attributed to reductions in short-distance trips, resulting in estimated reductions of 0.25% for PM, 1.17% for NOx, and 3.93% for CO emissions. These findings underscore the importance of assessing the potential benefits of railway expansion with tools that consider multiple data sources and models, and the need for integrated policies to achieve sustainable urban transportation systems.

User preference and willingness-to-pay for operation strategies that enhance safety and convenience of E-scooter sharing services

An electric scooter sharing (ESS) service offers improved convenience by providing accessibility to public transportation stations and mobility for short-distance trips. However, with the growing global popularity of ESS services, concerns related to safety and accidents have also increased. Previous studies explored the factors impacting preferences for ESS services; however, few have considered ways to enhance safety and convenience for users. Therefore, this study investigated the factors that influence people's choice of ESS service for enhancing safety and convenience. Data was collected in Seoul, Korea, and a latent class model was developed, allowing us to estimate the willingness-to-pay (WTP) of various attributes. The estimation results revealed that the “Exclusive e-scooter lane” attribute was the most preferred, followed by “provision of real-time driving information.” The WTP for “Exclusive e-scooter lane” was 47.1% above the base fare. In the estimation results of the class membership model, the preferred attributes were different depending on the class characteristics. The study further revealed variations in WTP for safety and convenience among users and non-users, as well as among non-users depending on their intent to use the ESS service. Furthermore, the proposed attributes constituted factors that affect the safety and convenience of ESS users, and that users were willing to pay an additional cost for them.

Deployment priority of public charging speeds for increasing battery electric vehicle usability

To inform charger deployment decisions, this paper aims to understand the potential utilization and deployment priority for public charging infrastructure. A data-driven Cumulative Public Recharging (CPR) model is developed to explore the travel patterns by using 2017 National Household Travel Survey data. Given the daily trip sequence, trip distance, and dwell times, the study examines the daily expected driving range and BEV feasibility under different charging speeds, battery capacity, and charging behaviors. The results suggest that more advanced public chargers increase the daily expected driving range. Home charging is sufficient for most daily short-distance trips while public chargers are still needed for medium- and long-distance trips. Extreme fast charging (xFC) may not be necessary for people with home charging but could be more useful for people without home charging and for urgent charging. xFC becomes even less important to drivers with longer BEV ranges, a finding that contradicts conventional thinking.

Analysing the Relationship between Proximity to Transit Stations and Local Living Patterns: A Study of Human Mobility within a 15 Min Walking Distance through Mobile Location Data

Urban planning and transportation policies are vital to creating sustainable and liveable cities. Transit-orientated development (TOD) has emerged as a prominent approach that emphasises the establishment of neighbourhoods with convenient access to public transportation, thereby promoting car-free lifestyles. This research investigates the connection between proximity to transit stations and local living habits in Auckland, New Zealand, which is a car-dependent city aiming to transition to a sustainable TOD model. We use geolocational data from mobile phones to measure the daily mobility patterns of residents living within a 15 min walking distance of various transit stations. Employing ordinary least squares (OLS) regression, we analyse the correlation between residents’ average travel distances and individual mobility, considering different station contexts. We aim to determine whether individuals living near transit stations are more inclined to participate in local activities and make a higher proportion of short-distance trips. The results illustrate that approximately 54% of the residents show dominant localised mobility patterns. Living near a station is significantly associated with shorter annual travel distances, although this trend varies by area. Notably, only about 16 of the 34 stations studied indicate that their local residents predominantly engage in ‘local’ travel patterns. Rural stations show less correlation, likely due to poor infrastructure and limited walkability. This study underscores the vital role of proximity to transit stations in promoting sustainable mobility. It serves as a foundational guide for urban planners and designers to make informed decisions that improve the built environment and optimise land use.

The factors affecting people’s willingness in participating in and paying for upgrading their seats from the economy class to the premium class

As information technology develops rapidly and network information becomes more and more transparent, it is urgent for airlines to increase their flight carrying rates through marketing to maximize profits before take-off. While the seat bidding strategy has been one of the business models adopted by many airlines in recent years, it has not been implemented yet in Taiwan’s airlines. In order to understand the seat bidding behavior of passengers, this study adopted the double-hurdle model to explore the factors affecting passengers’ participation in seat bidding and calculated the prices they were willing to pay. Moreover, this study investigated the effects of long and short flight distances on passengers’ participation in seat bidding and the prices they were willing to pay for either flight distance. According to the results, the factors affecting the participation of passengers in seat bidding were different for long-distance and short-distance trips, while a common factor between them was that young people and passengers with preliminary knowledge of such activities were more willing to participate. According to the analysis on the prices people were willing to pay, the price difference that passengers of long-haul flights and short-haul flights were willing to pay for upgrades was about 55 and 24% of the reference price, respectively. The information obtained in this study can serve as a reference for future research or for airlines.