Trip Distribution Research Articles

Collaborative optimization of multi-modal transport solutions for urban-rural bus routes.

Faced with the land use heterogeneity and trip distribution non-equilibrium along the corridor connecting urban district and rural area, the operation characteristics of the all-stop bus, the express bus and the shuttle bus running on the transportation corridor should be given full consideration, as well as the tradeoff between passenger trip cost and bus operation cost during the peak-hour period. In order to realize the reliable decision of bus resources configuration for urban-rural public transportation, a bi-objective programming model of multi-modal urban-rural bus operation scheme is established, where the decision variables include the departure frequency of different bus modals, the terminals of shuttle buses, and the stop scheme of express buses, under the constraints of departure interval, passenger volume, vehicle occupancy, section capacity usage and modal competition. To validate the feasibility of proposed optimization model, an urban-rural bus corridor from Rural Jasmine Ecological Park in Hanjiang District to West Xiangyang Bridge in Songqiao Town was chosen as a study case, and the operation organization scheme of three bus modals were solved by the mathematical solver Lingo using mixed integer programming. Meanwhile, the scheme differences under different subobjective weights were discussed, taking into account the uncertain passenger demand and operation supply. The result shows that the increase of passenger trip cost weight will increase the departure frequency of all three bus modals, while the increase of operation cost weight will decrease the total operating vehicles, but the stop frequency at middle stations for express buses will increase accordingly. Compared to the current operation scheme, the optimized scheme can greatly enhance the section capacity usage at a slight sacrifice of passenger trip time with fewer bus vehicles.

Towards Urban Accessibility: Modeling Trip Distribution to Assess the Provision of Social Facilities

Assessing the accessibility and provision of social facilities in urban areas presents a significant challenge, particularly when direct data on facility utilization are unavailable or incomplete. To address this challenge, our study investigates the potential of trip distribution models in estimating facility utilization based on the spatial distributions of population demand and facilities’ capacities within a city. We first examine the extent to which traditional gravity-based and optimization-focused models can capture population–facilities interactions and provide a reasonable perspective on facility accessibility and provision. We then explore whether advanced deep learning techniques can produce more robust estimates of facility utilization when data are partially observed (e.g., when some of the district administrations collect and share these data). Our findings suggest that, while traditional models offer valuable insights into facility utilization, especially in the absence of direct data, their effectiveness depends on accurate assumptions about distance-related commute patterns. This limitation is addressed by our proposed novel deep learning model, incorporating supply–demand constraints, which demonstrates the ability to uncover hidden interaction patterns from partly observed data, resulting in accurate estimates of facility utilization and, thereby, more reliable provision assessments. We illustrate these findings through a case study on kindergarten accessibility in Saint Petersburg, Russia, offering urban planners a strategic toolkit for evaluating facility provision in data-limited contexts.

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxis

ABSTRACT A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy for a given zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips’ destinations. Likewise, the destination-entropy for a given zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips’ origins. Furthermore, we have created an interactive geovisualization that enables researchers to delve into and juxtapose the spatial and temporal dynamics of origin and destination entropy, in conjunction with trip counts for both origins and destinations. Results indicate that entropy-based measurements effectively capture shifts in the diversity of trips’ geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic. These measurements, alongside trip counts, offer a more comprehensive understanding of urban human flows.

Using a spatial autoregressive model with spatial autoregressive disturbances to investigate origin-destination trip flows.

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance.

How to apply the four-step model for 150,000 travel zones: The HIPAT model

Current European transport demand models following the classical four-step approach are less suitable for analysing the traffic volumes on inter-regional road infrastructure with relevant demand of regional trips, as they consider only about 10% of the trip demand with passenger cars. Mainly due to runtime problems, these models are limited to operation at the NUTS-3 level. This level comprises around 1500 regions for Europe with an average diameter of about 50km. However, more than 90% of the trips are shorter than 50km and are, therefore, not adequately considered in the trip distribution model and the trip matrix. Computing this matrix at a higher spatial resolution, such as the LAU-2 level, is not possible as the runtime of the overall model and the size of the trip matrix increase quadratically with the number of travel zones. In the HIPAT model, this obstacle is overcome by an innovative concept operating at different hierarchical levels. This reduces the complexity of the trip matrix, such that the HIPAT model can be applied at large scale for 150,000 travel zones without any runtime problems. This is demonstrated by a prototype implementation for a case study that handles 33,000 travel zones in two minutes. In addition, the limitations of applying NUTS-3-based travel zones for transport modelling are shown: 94.6% of the trips are shorter than the average diameter of the travel zones (50km) and are therefore not correctly represented by the model. Smaller travel zones decrease this number and provide a better basis for analysing infrastructure policies and, ultimately, for decision-making.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.

Trends and determinants of the mobility of older adults in the USA and Germany, 2001–2017

Using the 2001 and 2017 National Household Travel Surveys (NHTS) for the USA and the 2002 and 2017 Mobility in Germany Surveys (MiD) for Germany, this paper examines differences between the two countries in the travel behavior of older adults and to what extent it has changed between 2001/2002 and 2017. We use five mobility indicators to portray the travel behavior of older adults: whether or not a trip is made at all on the survey day (i.e., mobile or not); number of trips per day for mobile individuals; km of travel per day for mobile individuals; modal distribution of trips; and trip purpose. In analyzing the travel behavior of older adults, we grouped them into 5-year age categories: from 60–64, 65–69, 70–74, 75–79, and then 80+ years. In addition to a descriptive, graphical analysis, we employ multi-level multiple regression analysis to examine factors that may affect the mobility of older adults.Mobility levels generally declined with increasing age in both countries in both years, but the declines were gradual, except for a steep drop for the 80+ age group. Mobility levels in 2001 were generally higher for all age groups in the USA than in Germany, especially for older adults. By 2017, however, the gap between the two countries diminished because mobility increased more for all age groups in Germany than in the USA from 2001/2002 to 2017. Indeed, Germans of all age groups in 2017 were more likely than Americans to make at least one trip during their travel day.The largest increases in mobility in both countries were for the oldest age categories (75–79 and 80+). From 2002 to 2017, the share of car trips among older adults in Germany rose considerably, while it fell slightly in the USA from 2001 to 2017. Nevertheless, car dependence among the oldest adults in 2017 was still twice as high in the USA as in Germany. One reason for Germany’s lower modal share of driving is that the transport system in Germany offers far more attractive alternatives to car use than in the USA, while policies at every government level make car ownership, parking and use more expensive and more difficult than in the USA.

A Spatiotemporal Comparative Analysis of Docked and Dockless Shared Micromobility Services

Sustainable urban mobility is an imperative concern in contemporary cities, and shared micromobility systems, such as docked bike-sharing, dockless bike-sharing, and dockless e-scooter-sharing, are recognized as essential contributors to sustainable behaviors in cities, both complementing and enhancing public transport options. Most of the literature on this subject predominantly focuses on individual assessments of these systems, overlooking the comparative analysis necessary for a comprehensive understanding. This study aims to bridge this gap by conducting a spatiotemporal analysis of two different shared micromobility modes of transportation, docked bike-sharing systems and dockless e-scooter-sharing systems operating in the municipality of Lisbon. The analysis is further segmented into arrivals and departures on weekdays and weekends. Additionally, this study explores the impact of sociodemographic factors, the population’s commuting modes, and points of interest (POIs) on the demand for both docked bike-sharing and dockless e-scooter-sharing. Multiscale Geographically Weighted Regression (MGWR) models are employed to estimate the influence of these factors on system usage in different parishes in Lisbon. Comparative analysis reveals that the temporal distribution of trips is similar for both docked bike-sharing and dockless e-scooter-sharing systems on weekdays and weekends. However, differences in spatial distribution between the two systems were observed. The MGWR results indicate that the number of individuals commuting by bike in each parish has a positive effect on docked bike-sharing, while it exerts a negative influence on dockless e-scooter-sharing. Also, the number of commercial points of interest (POIs) for weekday arrivals positively affects the usage of both systems. This study contributes to a deeper understanding of shared micromobility patterns in urban environments and can aid cities in developing effective strategies that not only promote and increase the utilization of these shared micromobility systems but also contribute to sustainable urban mobility.

Spatial Distribution and the Sustainability of Student Mobility of Top Universities in Bandung, Indonesia

As leading higher education institutions, top universities generate significant daily traffic. In addition, its high education quality often attracts youth to migrate and live closer to the university complex. The development of novel policies that support sustainable transport mode usage will become a role model and provide public benefits to the environment. Yet, there is little knowledge and limited research on university student mobility. This study aims to identify the significance of the daily trip distribution generated by top university students and identify the sustainability of its transportation modes by taking Bandung, Indonesia, as a case study. Responses from 383 students at 4 top universities were collected and then analysed using comparative analysis. The analysis reveals that 66,48 % of the students live in an area less than a 5 Km radius, yet, still depend on non-sustainable transport modes. This finding carries important policy implications to encourage more sustainable transport mode usage within this 5 Km area.

Improving the Profitability of Public Transport by Daily Forecasting of Passenger Loads and Bus Trip Distribution

The degree of utilization of passenger vehicle capacity affects the economic component of public transport operation. This is due to the fact that it depends on the number of passengers transported, as well as on the capacity of the passenger vehicle used for transportation. One of the tasks formulated in the state program of transport development of the Republic of Belarus is to achieve full self-sufficiency of public transport operation. One of the ways to achieve this goal can be maximizing the degree of use of passenger vehicle capacity by ensuring its compliance with the existing parameters of passenger flow. In this paper, based on the values of passenger flows in one of the cities of Belarus, obtained as a result of field observations in 2019 and 2024, we calculate an indicator that assesses the degree of utilization of passenger vehicle capacity, the factors that determine it, and the regularities of its change. Based on such calculations, the feasibility of creating a new public transport management technology based on daily forecasting of passenger traffic and distribution of passenger vehicles of different capacity to the routes taking this into account is shown.

Exploring transportation equity issues for persons with disabilities: The impact of gender on mobility and accessibility indicators

IntroductionPersons with disabilities still face numerous barriers in their everyday lives, especially in transportation. The problem of access to transportation is identified through fewer daily trips and more barriers in public transport. These problems indicate transportation equity issues among persons with disabilities. Another issue recognized in previous studies was a gender imbalance in the samples that could indicate gender equality problems among persons with disabilities. This paper aims to investigate mobility and accessibility indicators among persons with disabilities, with a particular emphasis on the impact of gender, in order to explore two critical transportation equity issues: inclusivity and social justice. MethodsThe mobility and accessibility indicators analyzed in this research were the Number of daily trips, Mobility satisfaction, Infrastructure accessibility satisfaction, and Modal distributions of daily trips. The data about indicators was collected by an online version of a questionnaire. The research was performed on 100 persons with disabilities from Serbia, equally distributed by gender. The most common recorded diagnosis of the respondents was spinal cord injury. ResultsIn general, respondents made 3.26 trips per day. At two transportation modes, we identified significant differences between males and females. Males frequently tripped as passenger car drivers, and females often traveled as passengers in passenger cars. Additionally, nine Bayesian linear regression models with weak informative prior distributions brought much helpful information. ConclusionsThe obtained results bring stakeholders valuable information in creating policies, practices, and recommendations to achieve specific goals related to transportation equity. Improving flexibility and time efficiency of females’ trips and resolving the problem of females’ fear of abuse and harassment in taxi cars were recognized as the two most critical topics.

Pedestrian-oriented development in Beirut: A framework for estimating urban design impacts on pedestrian flows through modeling, participatory design, and scenario analysis

As municipalities are setting ambitious targets to increase non-motorized transportation mode shares, analytic frameworks for examining how such targets will be met become increasingly important. In this article, we update the Urban Network Analysis pedestrian modeling framework designed to link land use and urban development changes with pedestrian mobility outcomes by introducing new methods for capturing the effects of street properties on pedestrian travel demand and adjusting pedestrian trip generation rates with k-nearest accessibility scores based on destination availability in each context. This framework was used in conjunction with a participatory design process in Beirut, Lebanon to estimate pedestrian mobility impacts of three urban design scenarios. We illustrate how each scenario affects pedestrian trip generation across various trip types and trip distribution on individual street segments. Our approach demonstrates how urban design interventions–both land use changes and street quality improvements–can influence pedestrian travel demand. Estimates of these changes can provide planners and policymakers with valuable benefit-cost analyses of public space improvements, and a framework for understanding how site-specific planning and development decisions can impact progress towards (or away from) non-motorized mobility goals.

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study.

Mobile data for studying public space, and trips in Mexico City: a study case of six suburban zones

Abstract This study uses anonymized GPS traces to explore travel patterns within six suburban zones and a central area in Mexico City. The descriptive analysis presented in this paper profiles trips by distance and investigates their distribution within each zone. It examines the prevalence of local trips, walkability, and the availability and spread of entertainment sites within 15-min isochrones accessible by foot, bicycle, transit, and private vehicle. Notably, the central zone boasts diverse entertainment offerings, commendable walkability, and a substantial proportion of short and long trips. It is found that GPS traces are within their home. However, the share of long trips for the inhabitants of central zones is considerably more significant than that for the suburbs. The study highlights suburban zones that could benefit from governmental intervention to enhance transportation and pedestrian conditions. Additionally, it identifies other suburban zones that resemble the central areas in terms of walkability, trip distribution by distances, and the accessibility of entertainment places.

The model validation for trip assignment using all or nothing and user’s equilibrium method

In transportation planning, an accurate transportation model is crucial. The trip-based approach is widely used, which relies on the four-step model (FSM) for transportation modeling. The model generates an origin-destination (O-D) Matrix as the starting point, which is then used to determine traffic flow on the road network through a trip assignment. This approach is superior because the (O-D) Matrix is a result of the interaction between land use and accessibility. In Boyolali Regency, research was conducted using traffic volume data that reflected the conditions at the beginning of 2023. The gravity model was used to determine trip distribution for this study, and the Least Squares method and negative exponential function calibrated the model. The study aim is to compare the performance of All or Nothing and User’s Equilibrium assignment methods using the EMME/3 software. The results showed that All or Nothing had a higher R2 value of 0.913 compared to User’s Equilibrium’s 0.911, suggesting that the All or Nothing assignment method is more suitable for Boyolali Regency. The research predicted that the total movement of Boyolali Regency in 2023 would be 12,354 pcu/hour, with most of the movement being external-external. As Boyolali Regency is a transit area, vehicles only pass through it.

The Impact of Online Transportation Services on Indonesian Urban Non-Working Trip Volume and Distribution Pattern: A Case Study in Bandung City

The development of digital technology has made it much easier for most people to access online communication through smartphones. Such a phenomenon has encouraged operators to develop an online transportation service system, which has also been growing in Indonesian urban areas. The urban community accepts the new alternative of the application-based transportation system since it offers a much more efficient way to reach transportation services. Moreover, the online transportation system also offers a much wider range of destination locations. It is believed that the creation of an application-based transportation service system will affect metropolitan areas' social and economic conditions and how urban communities’ travel. The development of this application-based transportation service would change the configuration of urban non-working trip distribution patterns, which should be understood as inputs for the urban transport development plan. This research aims to understand the impact of online transportation services on non-working trip distribution patterns in Indonesian urban areas, with Bandung City as the case study. The method used in this research is statistical descriptive, using respondents' responses to map the influenced trip generation and distribution patterns. The results show that online transportation has enabled the community to travel more easily and reach wider areas. Such changes have formed a new travel pattern that extends the destination locations to a wider peri-urban area. In the future, the government should anticipate urban fringe area land use growth, changing the region into more "urbanized-built-up" areas.

Calibrating walkability indicators for commute walk trips: A structural equation modeling approach

Walking plays a significant role in promoting public health, decreasing air pollution, and vitalizing cities. Walkability is an important notion that transportation planners and policymakers often use to quantify the suitability of and to promote travelers’ propensity for walking in an urban area. This paper adopts a data-driven approach using structural equation modeling for simultaneous estimation of 1) a latent construct of walkability using different walkability indicators (thus estimation of weights of the indicators), and 2) the effect of walkability on commute walking behavior for an urban area. Doing so accounts for the effects of various indicators of walkability as well as socio-economic and security characteristics. A Partial Least Square Structural Equation Model is developed to estimate both direct and indirect effects of walkability on the share of commute walk trips in total commute trips for the city of Chicago using census tract-level data. We find that walkability is the most important factor affecting the share of commute walk trips. Among the indicators of walkability, transit- and job density-related indicators are the most important ones. Some socio-economic factors, especially travel time, vehicle ownership, and college student share, also critically influence the propensity for walking to commute. We further find that our calibrated walkability score results in a different hotspot distribution in Chicago than using the uncalibrated walkability score, as is currently practiced by the Chicago Metropolitan Agency for Planning, but is more consistent with hotspot distribution of the actual commute walk trips in the city. These findings advance the understanding and characterization of walkability and actual walking behavior in urban areas, which in turn helps inform pedestrian infrastructure investment and policy making.

Trip Distribution and Modal Choice in the Taman Mini LRT Station Area

The surrounding Taman Mini LRT station boasts a diverse range of land uses, primarily characterized by residential zones, interspersed with dynamic activity hubs such as tourist attractions, shopping malls, and hospitals. The prevailing issue in this area relates to persistent traffic congestion, stemming from the overreliance on private vehicles, inadequate public transportation infrastructure, disjointed intermodal connections, and the lack of seamless integration between transportation modes and activity centers. This study endeavors to comprehensively analyze pedestrian and vehicular movement in the periphery of the Taman Mini LRT station. This analysis is pivotal in assessing the current mobility landscape. Notably, the intensity of activity within a particular land use directly correlates with its ability to draw vehicular traffic, consequently leading to prolonged waiting times for pedestrians at crosswalks. Employing a four-step model, this research methodically defines pedestrian and vehicular flow within the region. This model incorporates output distribution of trips and origin-destination graphs. Additionally, stated preference techniques are employed to discern respondents’ inclinations when making travel decisions. This involves evaluating key factors like travel time, fares, frequency, accessibility level, comfort, and safety – categories that significantly influence their preferences when selecting between LRT and other transportation modes.

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.