Trip Distribution Research Articles

Traffic impact analysis PT. Eastern Pearl Flour Mills – City Side Jl. Nusantara, Makassar

<span>PT. Eastern Pearl Flour Mills – City Side Makassar affects land use changes that have an impact on changes in traffic movement, so traffic impacts analysis is needed. The purpose of this study is to identify the performance of affected roads and intersections in the present and in the next 5 years, to handle traffic problems and conflicts by simulating models without handling (Do Nothing) and with handling (Do Something), analyzing time delays on the U-turns and modeling the origin-destination matrix using the Furness Method. This study uses qualitative and quantitative analysis methods that refer to the Manual Kapasitas Jalan Indonesia (MKJI, 1997) and The Furness method. The results of the analysis of the level of service on the Jl. The archipelago is in fairly good condition with LoS values A to B and DS 0.11 – 0.40 with a speed of 26.60 – 50.00 km/hour, the road performance that has the worst impact occurs at the Tol Reformasi – Jl. Nusantara with a projection for the next 5 years in 2027, where road performance has a LoS E and DS value of 0.88 with a delay time of 19 seconds/pcu. U-turn facility on Jl. Nusantara has an average delay time of 6.16 – 7.82 seconds for one rotating vehicle with a value of < 1 so that there is no queue. The results of the trip distribution analysis using The Furness model obtained the same value of generation and attraction in the second iteration, which means that it is still close to ideal conditions.</span>

Analyzing non-linear contributions to predictive performance in a neural network based scheduling model

This paper aims to investigate whether increasing non-linear opportunities in a neural network-based scheduling model improves its predictive performance. More specifically, this paper experiments on a trip distribution model that is part of an activity-based scheduling model called Skyline-seqNN from the ongoing thesis A neural network scheduling model. The motivation behind that model's proposed structure is to lay the groundwork for a neural network discrete choice model (DCM) that achieves to model travel demand on a detailed level while also being suitable for experimental analysis.Similar to a four-step model framework in the sequential aspect, the model system from the referenced paper utilizes the three sub-models; trip generation, trip distribution, and mode choice using a utility-maximizing micro-simulation approach. The trip generation model first decides whether, at every 10-minute interval between 05:00 am and 11:00 pm, an individual in the next time step should stay and continue the current activity or take an activity-defined trip. The distribution and mode choice models are used whenever a trip is selected. The trip distribution model decides the trip's destination by evaluating travel times and land use descriptions of each zone. The mode choice model learns the probability distribution of modes given each mode's travel time to the selected destination zone.Tests performed in this paper show how successive non-linear opportunities between input features in the trip distribution model increase its predictive performance. The data used for training and evaluation comes from a travel questionnaire from 2015 performed in Stockholm containing 10819 individuals and days.

Mobility patterns after the pandemic: a survey in 20 European cities

The results of a wide survey with 10 000 participants in 20 European cities suggest that mobility patterns in 2021 were still affected by the Covid-19 pandemic. The survey explores the factors that influence the respondents’ choices as regards trip generation, distribution and modal choice. The behavioural changes resulting from mobility restrictions in 2020 and the adoption of technological alternatives such as teleworking, video-conferencing, or online shopping limited the number of trips, but the gradual return to normality is also leading to a rebound in transport activity. The results reveal an increased use of cars for daily urban mobility, which may be sustained in the future. The main reason seems to be an increased level of users’ risk aversion and their avoidance of public transport or other shared modes. Such a trend can be also alarming as regards the future prospects of emerging technologies and business models, e.g. micro-mobility or Mobility-as-a-Service applications.

Оцінка пропускної спроможності міської дорожньої мережі з урахуванням пропозиції паркування

In order to improve the estimation of network capacity for a given urban road system, it is assumed that the impact of destination parking management strategies should be considered in the network capacity estimation model. This study presents an extended road network capacity model that takes into account both the number of parking spaces and parking rates in each traffic zone. The network capacity model is formulated as a two-level programming problem with maximizing the total trip generation at the upper level and the combined trip distribution and traffic assignment (CTDTA) problem at the lower level. In order to reasonably characterize the impact of parking pricing and parking delays due to congestion, the CTDTA model involves two classes of travel demand. An efficient and practical algorithm for solving the two-level network capacity model is presented. Numerical experiments show the advantages of the proposed model and demonstrate the impact of parking supply and pricing on the results of road network capacity assessment.

СВЯЗЬ МАКСИМИЗАЦИИ ЭНТРОПИИ И КОНЦЕПЦИИ ЭКОНОМИЧЕСКОЙ МОДЕЛИ ПОЛЕЗНОСТИ В ТРАНСПОРТНОЙ СИСТЕМЕ

The aim of the article is to study the relationship between maximum entropy methods and the concept of economic utility in the distribution of trips in a transport system

Accessibility and Public Transport Mobility for a Smart(er) Island: Evidence from Sardinia (Italy)

• Multimodal integration model for Public Transport Service (PTS) in an island scenario • Mobility-based spatial interpretation for the benefit of the population • Use of real data obtained by the Kriging method • Transport modes do not reflect the socio-physical and economic reality of the island The purpose of this paper is to highlight the critical aspects of islands context transportation accessibility by suggesting a more innovative, safer, and more sustainable framework for public transport service (PTS) and place-based organisation, as well as by integrating the latest tendencies in mobility. Specifically, this research focuses on multimodal integration models for PTS that consider the existing infrastructure system and the socioeconomic issues typical of an island environment. This topic has received inadequate consideration in the scientific literature on islands. To achieve these aims, an analytical-numerical approach is adopted. Starting from initial origin-destination matrices (O/D) surveyed by the National Institute of Statistics (ISTAT), a methodology was implemented to compute the geographical distribution of trips, and thereafter, the values distributed over the whole region were interpolated by Surfer software with the Kriging method. This methodology was applied to the Sardinia case study particularly emblematic because it has seen a tremendous transition over the previous decades, resulting in a massive socio-economic gap between inland and coastal areas, that led to an increase in private vehicles for transportation purposes, primarily for business and pleasure. The application, replicable in other islands that have highlighted the same socio-economic problems linked to poor mobility planning, shows an accessible spatial planning approach, combining PTS and rental for driver services, by considering the principles and issues of island contexts. This research gives an important scientific contribution by emphasising the quality of transport infrastructures, place-based organisation, population distribution, and physical configuration of the Insular Region, as well as by considering the most pressing issues of island contexts. Findings could help island governments in revising their policy and practice of transport, accordingly.

Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms

Building an accurate model of travel behaviour based on individuals’ characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton’s future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability.

Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes

Given the increasing prevalence of new urban transport modes such as ridesharing, e-hailing, and combined transport, it is essential to evaluate their effects on the capacity of transportation networks. Hence, this paper develops a novel transportation network capacity model to capture the travel behaviors of inter-multimodal mobility in an urban transportation system that incorporates emerging travel modes. The novel model is formulated as a bi-level programming problem, in which the lower-level model is a combined modal split and traffic assignment (CMSTA) problem based on mathematical programming. The CMSTA problem adopts the cross-nested logit model to account for intermodal travel behavior in the modal split phase and the path-sized logit model to account for route overlap in the traffic assignment phase. Moreover, the logit-based trip distribution model is used to capture the dispatch of the e-hailing traffic flow and the matching of ridesharing drivers with passengers. Besides, we consider flow interactions (e.g., cars and buses sharing the same link) in the road network. We customize a solution framework for solving this novel model that adopts the recently developed fast path-based algorithm with the Barzilai–Borwein stepsize strategy to efficiently solve the CMSTA problem, and derive a sensitivity analysis-based (SAB) algorithm to solve the entire bi-level programming problem. The effectiveness of the novel model is verified in numerical experiments that demonstrate the effects of intermodal transportation, e-hailing, and ridesharing on the capacity of a multimodal transportation network.

Analysis of the Factors Affecting the Construction of Subway Stations in Residential Areas

To design a more suitable scheme under different conditions so that subway stations can play their role better, this study investigated the construction elements of subway stations in residential areas. Metro stations in residential areas are generally located at the intersection of urban roads. As the “handover space” of the city, the construction principle should be based on people’s experience. As the core basis for traffic planning and future operation management, construction of subways in residential areas should take into consideration factors such as trip volume, distribution, and mode selection of residents, and be determined based on such mathematical models as the unit factor method, gravity model, and disaggregated analysis method. Station site selection is based on the point, line, and surface elements, and the importance of a station in the line network is judged by degree and betweenness centrality; the accessibility of the line network is determined by connectivity. In this study, the influencing factors of residential area subway station construction are divided into construction elements and site selection. Internal construction elements of stations include station entrances and exits, escalators, ticket machines, and transfer routes, and the conclusion of the mathematical model is used to select or give opinions about the internal construction elements of the subway. The point, line, and surface elements and the connection relationship between the subway and buses are used to determine the site selection of the subway. Furthermore, this paper discusses the three elements that affect the construction of the subway, comprehensively considers the functional requirements of the subway, and makes reasonable adjustments to each element. Finally, the requirements for the elements of the subway construction are determined.

Estimating Energy Savings from Bus Improvement Options in Urban Corridors

The potential to achieve significant energy savings in the road transport sector can be a powerful driver to promote bus transport, especially bus rapid transit (BRT) development. This research introduces a spreadsheet tool for making realistic estimates of energy savings due to increased use of buses, with an explicit inclusion of the effect of congestion on traffic flow and fuel consumption. Based on scenarios developed around projected growth in trip demand, changes in vehicle technology, lane expansion, and modal distribution of trips, the model determines typical daily profiles for fuel consumption by vehicle types. A case study has been performed on an urban corridor in the city of Kuala Lumpur to compare energy usage among three scenarios: business as usual, conventional bus lane, and full-scale BRT implementation. The BRT provides significant energy savings over both alternatives, with the greatest savings achieved when locating the BRT in a newly constructed lane.

Simulating inter-individual contact in the inter-station passenger transfer system connecting multiple metro stations based on space–time path data

Determining passengers’ inter-individual contact in the metro station area (MSA) is an important issue to simulate and mitigate the spread of the Coronavirus disease 2019 (COVID-19) pandemic. Taking the inter-station passenger transfer system (IPTS) as an example, this study aimed to verify the passenger flows’ influence on the inter-individual contact around the MSA. Based on actual observed data, the passengers’ space–time paths (STP) in the network were obtained through an agent-based simulation. In this study, the direct contact model and the mediate contact model were used to describe the inter-individual contact in view of the passengers’ STP. The contact count and the exposure duration were defined as indicators to measure the contact degree of individual and the system. The results show that the time-varying trip distribution of the metro passengers significantly affected the inter-individual contact degree and the spatial distribution of contact risk region in the MSA. The intersection of passenger flow in different directions and the concentrated movement of passenger flow in the same direction increased the inter-individual contact and prolonged exposure in the morning. Through simulation experiments, the study verified the effects of controlling the flow direction and equalizing passenger flow generation measures aiming to reduce inter-individual contact and cumulative exposure duration.

Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS

The efficiencies of urban transport systems in several cities are drastically affected due to difficulties imposed by rapid urbanization and the proliferation of private modes of transport. The conventional four-stage travel demand modeling approach provides an ideal platform to formulate strategies to rectify problems in urban transport. Trip generation is the first stage in this exercise (where trip production and trip attractions are modelled), followed by trip distribution in the second stage. The present work related to the development of models for trip generation and trip distribution necessitated the use of census data related to the number of households in each zone since the available revealed preference (RP) data compiled based on household interview surveys was partially incorrect. A review of the literature indicated that studies on the use of sparsely available and partially inaccurate data such as revealed preference and zone-specific secondary data on trip generation and trip distribution were limited. In the present study, the use of the initial trip generation regression models developed based on existing household survey data resulted in prediction errors ranging between 26% and 32%. Modeling efforts after applying corrections to zone-specific characteristics based on secondary data and the use of trip rate per household later resulted in prediction errors of less than ±5%. In the latter phase of work related to trip distribution modeling, a log-linear regression model was developed based on a smaller refined set of the revealed preference data obtained by eliminating erroneous data in a stage-wise manner. The use of the calibrated and validated model ensured that the errors in predicted trip frequencies were less than 0.6%. Here, the information on the inter-zonal aerial distances that formed part of the trip distribution model was obtained using GIS approaches that employed the moment area method, which considered the intensity of land use at the sub-zone level. The combined strategy incorporates the use of GIS-based approaches to determine inter-zonal aerial distances, and the use of the refined relationship between trip interchanges and the inter-zonal aerial distances in the development of a reliable log-linear regression model for trip distribution contributed towards attaining higher accuracies in travel demand estimation. The modeling approaches described herein do not rely on the use of sophisticated technology, and time-consuming data processing. The study will provide the basic framework for transport planners to formulate better strategies for travel demand modeling where available data is noisy and less reliable. Doi: 10.28991/CEJ-2022-08-09-013 Full Text: PDF

Disaggregated approach to urban trip distribution: a comparative analysis between artificial neural networks and discrete choice models

Discrete choice models have been used over the years in disaggregated approaches to forecast destination choices. However, there are important constraints in some of these models that pose obstacles to using them, such as the Independence of Irrelevant Alternatives (IIA) property in the Multinomial Logit model, the need to assume specific structures and high calibration times, depending on the complexity of the case being evaluated. However, some of these mentioned constraints could be mitigated using Mixed Models or Nested Logit. Therefore, this paper proposes a comparative analysis between the Artificial Neural Network (ANNs), the Multinomial and Nested Logit models for disaggregated forecasting of urban trip distribution. A case study was conducted in a medium-sized Brazilian city, Santa Maria (RS), Brazil. The data used come from a household survey, prepared for the Urban Mobility Master Plan. For the sake of comparison, hit rates and frequency of trip distribution distances were analyzed, showing that ANNs can be as efficient as the Discrete Choice models for disaggregated forecasting of urban trip destination without, however, assuming some constraints. Finally, based on the results obtained, the efficiency of ANNs is observed for predicting alternatives with a low number of observations. They are important tools for obtaining Origin-Destination matrices from incomplete sample matrices or with a low number of observations. However, it is important to mention that discrete choice models can provide important information for the analyst, such as statistical significance of parameters, elasticities, subjective value of attributes, etc.

EXPLORING JITTERING AND ROUTING OPTIONS FOR CONVERTING ORIGIN-DESTINATION DATA INTO ROUTE NETWORKS: TOWARDS ACCURATE ESTIMATES OF MOVEMENT AT THE STREET LEVEL

Abstract. Origin-Destination (OD) datasets provide vital information on how people travel between areas in many cities, regions and countries worldwide. OD datasets are usually represented geographically with straight lines or routes between zone centroids. For active travel, this geographic representation has substantial limitations, especially when zone origins and centroids are large: only using a single centroid origin/destination for each large zone results in sparse route networks covering only a small fraction of likely walking and cycling routes. This paper implements and explores the use of jittering and different routing options to overcome this limitation, thereby adding value to aggregate OD data to support investment in sustainable transport infrastructure. The route network results — generated from on an open dataset representing cycling trips in Lisbon, Portugal — were compared with a ground-truth dataset from 67 count locations distributed throughout the city. This approach enabled exploration of which jittering parameters and routing options lead to the most accurate route network results approximating the real geographic distribution of cycling trips in the study area. We found that jittering and disaggregating OD data, combined with routing using low level of traffic stress (quieter) preferences resulted in the most accurate route networks. We conclude that a combined approach involving 1) jittering with intermediate levels of disaggregation and 2) careful selection of routing options can lead to much more realistic route networks than using established OD processing techniques. The methods can be deployed to support evidence-based investment in strategic cycling and other sustainable transport networks in cities worldwide.

The Influence of Public Transportation Stops on Bike-Sharing Destination Trips: Spatial Analysis of Budapest City

This research looks at how spatial factors of public transportation influence the use of bike-sharing in an urban context. Based on a grid-cell-based method, ordinary least square regression and geographically weighted regression (GWR) are developed to reveal the link between the spatial distribution of bus, tram, rail stops, and bike-sharing trips. The public transportation coverage in Budapest City is rated as excellent, with all parts of the city covered within a 5 km radius. We find that areas with a high proportion of public transportation stops have a significantly higher number of bike-sharing trips. Bike-sharing trips are concentrated near regional railway stations, the central business district, and surrounding zones. The connection between bike-sharing and trams/rails appears to be stronger than the connection between bike-sharing and buses. According to the findings, nearly one-third of public transportation stops have accessible bike docks within 125 m walking distance. In GWR analysis, the coefficients of bus stops are increasing towards the center of the city, while the coefficients of tram/rail stops are decreasing. Finally, by examining the priority zones for establishing more bike-sharing facilities, it is discovered that the eastern side of the city requires more development than the western side because it has a high number of bike-sharing trips but no adequate facilities near public transportation facilities.

Applications of Passive GPS Data to Characterize the Movement of Freight Trucks—A Case Study in the Calgary Region of Canada

The movement of trucks represents a significant portion of travel. Surveys have traditionally been used to measure truck movement, but this costly and limited data collection method typically involves in-person interviews and requiring high workload. This study explores different ways in which passive truck GPS data can be used to complement traditional data collection methods, for obtaining detailed information about the travel behaviors of freight trucks. First, we develop a heuristic-based model to identify truck stops. A new methodology is proposed to classify truck stops into a primary or secondary sto, which make identifying trip purposes possible. Primary stops are defined as the locations where the loading or unloading of the goods takes place. Secondary stops are those associated with all other purposes, including refueling, and driver breaks. Finally, we develop a destination choice model for modeling truck movements. This model applies a discrete choice modeling technique to distribute truck trips within the Calgary region, Canada. We test the utility function in the destination choice model to include of business establishment data, travel impedances, and other dummy variables that are likely to influence truck demand. The results show that a combination of trucks’ dwelling times and their entropy can be used to classify truck stops by purpose. This study also shows the potential of using passive GPS data to gain additional insights into truck movements characterization and truck trip distribution modeling.

Shifting the population mobility of the Ukraine western region on the strength of the COVID-19 pandemic

The Covid-19 pandemic has significantly affected the economic and social spheres of all countries. Restrictions intro-duced to reduce the risk of transmission have changed the structure of population movements. The impact of these restrictions on the characteristics of intercity travel is still an understudied problem. Based on the analysis of statisti-cal data and the results of questionnaires, the article assesses the impact of pandemic restrictions on population mobil-ity in the Western region of Ukraine and changes in the distribution of passenger flows between different modes (bus, rail, private transport, joint travel). In 2020, the volume of passenger traffic in the region decreased by an average of half compared to the previous year. The decline is sharper for rail passenger transport compared to the bus transport. For more developed railway networks, the impact of the pandemic on passenger traffic is more pronounced. Quarantine restrictions have also increased the share of own car travel. According to research, the distribution of intercity trips between modes is influenced by the age and sex of the traveler. During the pandemic, users of transport services who travel with children under the age of 14 choose private transport to travel more often than those who travel alone. The degree of influence of the above factors on the distribution of modes depends on the length of the trip. The application part of the work presents the results of modeling passenger flows of the studied region in the software environment PTV Visum. It was found that at the beginning of the quarantine restrictions the number of intercity trips decreases sharply. As the duration of restrictions increases, the rate of decline in mobility decreases. These data can be further taken into account when planning the work of transport enterprises and meeting the population`s demand for travel. The practi-cal application of the study results is that the identification of trends in the mobility of residents of the studying region depending on the impact of pandemic restrictions allows you to predict the mode and type of vehicles used. Based on these data, you can determine marketing strategies for the development of certain modes and directions of transporta-tion.

Revealed and future travel impacts of COVID-19 in sub-Saharan Africa: Results of big data analysis and a Delphi panel survey

Background: After coronavirus disease 2019 (COVID-19) was declared a pandemic, movement restrictions were implemented across sub-Saharan Africa. There has been much speculation on what the long-term impacts on urban transport might be. Objectives: The aim of this paper is to identify the revealed and future travel impacts of the pandemic. Method: To pursue this aim, evidence was compiled from two sources: secondary big data; and a ( n = 15) two-wave Delphi panel survey of experts in the region. Results: It is predicted that longer-term impacts will take the form of: reduced travel by, and accessibility for, low-income households residing in peripheral locations because of decreased welfare; reduced transport service availability; operator reduction (particularly amongst unsubsidised formal operators); increased remote activity participation for a minority of better resourced households with white-collar workers; and disrupted trip distributions as the mix of city-centre land use changes in response to business attrition in economic recession rather than to disrupted bid rents. Conclusion: The major impact of the pandemic is likely to be on welfare, rather than on trip substitution. There is a need, therefore, to focus policy on the mitigation of these impacts and, more particularly, on ways of measuring changes in transport disadvantage and exclusion so that reliable data are available to inform mitigation strategies. The mitigation strategies considered should include investment in affordable ‘digital connectivity’ as a means of complementing accessibility from physical proximity and mobility. The pandemic also highlights the need to develop more robust transport planning practices to deal with uncertainty.

Analysis of the change in the number of passenger seats of urban passenger transport depending on the gravity function

Current conditions for ensuring the implementation of urban mobility plans require high-quality functioning of urban passenger transport systems considering the minimising travel time and increasing travel comfort, as these factors affect passenger fatigue and their productivity during the working day. The study of changes indicators of the public transport system functioning from the gravity function is carried out in this paper, and the results of these studies are given. Based on the surveys of Lviv residents, the distribution of trips by public transport, private transport and taxis was carried out. It is determined that most often city residents use public transport (the share of such trips is 67% of all movements made by transport). An approach has been developed to determine the number of public transport vehicles depending on their capacity through the total number of passenger seats taking into account this distribution. This approach allows determining the required number of vehicles on the route and using different vehicles by their capacity depending on the passenger flow on the route to ensure the comfort of the trip. Based on this, the analysis of the change in the total number of passenger seats from the indicator of the gravity function for vehicles of different capacities is carried out. In addition, an analysis of the regularities of changes in the number of passenger seats for different by capacity vehicles is carried out, and mathematical models of these regularities are developed. It is determined that at low values of indicator of the gravity function, there is a need for large capacity vehicles, while with the increase in this indicator, the demand for small and medium capacity vehicles increases from 34% to 52%. The developed mathematical models can be used for practical justification of the required number of vehicles on the route, taking into account their capacity to ensure the comfortable movement of passengers on the routes of urban passenger transport.

Determining the Type Of Skybridge From The Bojonggede Station to The Bojonggede Terminal Considering The Traffic Impact

Bojonggede Station, located in Bogor Regency, is one of the KRL stations used to support the mobility of the surrounding community. This makes Bojonggede station a place with quite complex transportation problems. From the various problems that occur, this research aims to find the right sky bridge to connect the bojonggede station with the bojonggedeTerminall and reduce the impact of traffic in the surrounding location after the sky bridge. The data analysis method used is the 4 step model method consisting of trip generation/attraction, trip distribution, mode selection (Moda Split), trip assist, Indonesian road capacity manual, and descriptive analysis of survey data on visitors Bojonggede station. From the results of the analysis based on the 4 step model and manual of Indonesian road capacity, it is found that the performance of the Bojonggede 2 highway, which is in front of the location, has increased from LOS F during the construction period to LOS C during the sky bridge operations bridged. The benefit of this research is that it can be seen that traffic problems that occur at the study site can be overcome by the existence of an efficient sky bridge design sky bridges Bojonggede station with Bojonggede Terminal, to be further recommended to the Bogor district transportation office, and the Jabodetabek transportation management center ( BPTJ).