Travel Time Matrix Research Articles

A travel time matrix data set for the Helsinki region 2023 that is sensitive to time, mode and interpersonal differences, and uses open data and novel open-source software

Travel times between different locations form the basis for most contemporary measures of spatial accessibility. Travel times allow to estimate the potential for interaction between people and places, and is therefore a vital measure for understanding the functioning, sustainability, and equity of cities. Here, we provide an open travel time matrix dataset that describes travel times between the centroids of all cells in a grid (N = 13,132) covering the metropolitan area of Helsinki, Finland. The travel times recorded in the dataset follow a door-to-door approach that provides comparable travel times for walking, cycling, public transport and car journeys, including all legs of each trip by each mode, such as the walk to a bus stop, or the search for a parking spot. We used the r5py Python package, that we developed specifically for this computation. The data are sensitive to diurnal variations and to variations between people (e.g. slow and fast walking speed). We validated the data against the Google Directions API and present use cases from a planning practice. The five key principles that guided the data set design and production – comparability, simplicity, reproducibility, transferability, and sensitivity to temporal and interpersonal variations – ensure that urban and transport planners, business and researchers alike can use the data in a wide range of applications.

Public transport accessibility indicators to urban and regional services in Great Britain

Public transport accessibility to urban and regional services has been found to relate to various social and economic processes, such as unemployment, transport mode choice, property prices, and public health. A frequent type of measures representing accessibility are location-based. While these offer advantages, like flexibility and ease of interpretation, their estimation usually requires specialized skills and substantial computational resources. To lower these barriers, we have prepared a suite of accessibility indicators for key services across Great Britain at a spatially disaggregated level. The dataset includes ready-to-use public transport accessibility indicators for employment, general practitioners (GP, or family physician), hospitals, grocery stores, supermarkets, primary and secondary schools, and urban centres. It also includes the raw travel time matrix from each origin to every potential destination, a primary input for such indicator estimation. Altogether, this resource offers various levels of application, from direct input into a range of research topics to the foundation for creating comprehensive custom indicators.

Unraveling social inequalities in urban health care accessibility in Montevideo: A space-time approach

ABSTRACT Access to urban health care opportunities is a major factor in explaining health outcomes. We propose an innovative methodology to analyze spatio-temporal accessibility considering spatial, temporal, and individual variability on both the demand and the supply side. To construct the measure, we use three primary sources: an exhaustive survey of the space-time provision of pediatric services in Montevideo (i.e., a survey of the times when pediatric care appointments are available at particular clinic locations), a representative sample of pediatric health care users, and a fine-grained travel-time matrix for different transport modes. Our results show that, while different socioeconomic groups have similar levels of access at the aggregate level, access is not evenly distributed. Poorer households compose the highest percentage of households facing a critical lack of access. In addition, we find that space-time constraints associated with employment limit access for low-socioeconomic households more so than for other socioeconomic groups. This methodological contribution allows us to reveal existing inequality patterns in health care access and indicates the need for policymakers to consider the space-time dimensions of access when developing health care policies.

Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data

Urban road travel time estimation and prediction on a citywide scale is a necessary and important task for recommending optimal travel paths. However, this problem has not yet been well addressed: most existing approaches face serious data sparsity issues, e.g., lack of sensor data on several road segments; and it is a difficult task to capture context patterns around the road and incorporate context-aware information into travel time estimation models. Because of this, we propose to utilize trajectory data to model road travel times as this type of data covers more urban road segments than data from traditional traffic monitoring systems. Moreover, the trajectory itself has involved both travel times and the context of road congestion. A general framework for context-aware road travel time estimation (CARTE) is then put forward. Specifically, we adopt a third-order tensor to model spatiotemporal road travel times by setting the congestion level as a third dimension. By incorporating another context-aware information, namely points of interest (POI), a coupled tensor decomposition algorithm is proposed to fill in missing data. Eventually, we propose an algorithm to calculate an ultimate two-dimensional (spatial and temporal) travel time matrix by weighting the congestion probabilities of each congestion level. The effectiveness of the CARTE was validated on two real-world datasets and it was compared to the state-of-the-art methods. The experimental results demonstrate that the proposed travel time prediction approach always achieves the best performance in terms of accuracy with different data sparsity and prediction horizons.

Spatial and social inequities for educational services accessibility - A case study for schools in Greater Mumbai

Education is one of the crucial aspects of an individual's life, contributing to economic and social well-being. Lack of adequate access to educational services to children can affect their overall well-being. Further, an inadequate supply of educational opportunities can reinforce social inequalities. This research develops a conceptual framework to measure accessibility for educational services using an extensive dataset of mode-wise travel time matrix of 577 Traffic Analysis Zones (TAZ) and 4308 schools in Greater Mumbai. We evaluated spatial and social equity from urban and transportation planning perspectives for access to educational services. The Gini index is used to assess the distribution of educational accessibility and quantify the spatial inequity. Social inequity is quantified based on the mode-wise accessibility ratio for educational services. The average accessibility ratio shows that the gap in access by public transport and private mode reduces as the threshold increases. A decision framework is developed based on accessibility measures, and policy measures are suggested to address the existing inequities. This study can help planners and policymakers effectively prioritise the suggested measures to maximise the benefits and address spatial and social inequities for access to educational services.

Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region

Comparable data on spatial accessibility by different travel modes are frequently needed to understand how city regions function. Here, we present a spatial dataset called the Helsinki Region Travel Time Matrix that has been calculated for 2013, 2015 and 2018. This longitudinal dataset contains travel time and distance information between all 250 metres statistical grid cell centroids in the Capital Region of Helsinki, Finland. The dataset is multimodal and multitemporal by nature: all typical transport modes (walking, cycling, public transport, and private car) are included and calculated separately for the morning rush hour and midday for an average working day. We followed a so-called door-to-door principle, making the information between travel modes comparable. The analyses were based primarily on open data sources, and all the tools that were used to produce the data are openly available. The matrices form a time-series that can reveal the accessibility conditions within the city and allow comparisons of the changes in accessibility in the region, which support spatial planning and decision-making.

Going to the nearest hospital vs. designated trauma centre for road traffic crashes: estimating the time difference in Delhi, India

Background: Time to hospital after a road traffic crash (RTC) plays a vital role in determining the outcome for crash victims. In Delhi, there are seven designated trauma centres where crash victims are typically taken, which may not be nearest hospital. We compare the transport time access (crash to hospital) depending on whether the victim is transported to a designated trauma centre or the nearest hospital.Data and methods: For each RTC geocoded manually from police records, the nearest hospital and the designated trauma centre is identified using Google Maps places nearby Search API and guidelines. Travel time matrix is generated between RTC’s and identified hospitals using Google maps distance matrix API. Index accounting inter-district differences is developed.Results and conclusions: The network of designated trauma centres in New Delhi is located such that they can be accessed within 45 min of most crashes while nearest hospital within 30 min. As a result, the vast majority of crash victims are likely to receive timely care if they are rapidly transferred to either of these caregivers. However, for the most severely injured and time-sensitive cases, bypassing nearest hospital for trauma care, could substantially improve survival outcomes.

Sebaran Pergerakan Pengguna Kendaraan Pribadi Bekasi Barat-DKI Jakarta

The high traffic in OKI Jakarta is not only influenced by mavements in but also commuters from sub-urban areas like Bekasi District. For that we need to know the characteristics and distribution of the movement from North Bekasi to Jakarta. The purpose of this study was to determine the distribution of travelling from Bekasi to Jakarta. The research methods used are the establishment of Inter-Zone Travel Time Matrix, trip generation modeling (multiple regression analysis), Travel Distribution Modeling. In addition, the quantity method is used to identify travel time, total travel costs and advanced modes are used when using public transport. Based on the results obtained by the analysis of trip generation models Y = -1.44 of public transport + 2:14 of private vehicles. Trip distribution of Bekasi's population towards OKI Jakarta are Central Jakarta (261 trips/day), East Jakarta (180 trips/ day), South Jakarta (125 trip/day), West Jakarta (74 trips/day), and East Jakarta (87 trips/day). The different of travel time between private vehicles and public transport is average 30 minutes. Private vehicle users must expend more vehicle surcharge of Rp. 11 553 which compared with public transport.

2392 - Multimodal Accessibility and Maternal-Infant Health across the Urban Rural Continuum in Southwest Michigan

Background Kalamazoo County, Michigan is an urban-rural county with high rates of gestational diabetes among expectant mothers and high disparities in the quality of birth outcomes among its population segments. Analysis of health outcomes in the county has primarily focused on densely populated areas of low socioeconomic status. However, the county has a clear urban to rural continuum. Because women without appropriate access to healthcare during particular stages of pregnancy have poorer outcomes than women with regular access to care, this paper examines the disparities in transportation network accessibility by public transit and private vehicle across the urban-rural continuum. Multimodal accessibility estimates were analyzed especially for individuals relying on Medicaid for perinatal health care from 2012–2014. Methods Reported, confirmed cases gestational diabetes, low birthweight infants and premature births for Kalamazoo County, were retrieved from 2012-14 Michigan birth records. For each case, the dataset includes mother's home address, Medicaid status, race, ethnicity, and an estimate of prenatal care. Batch geocoding was supplemented with extensive manual placement, resulting in an overall address match accuracy of over 90% of cases. Open Trip Planner and Google Maps JavaScript API were used to estimate travel time from census block centroids to pedicatric, obstetric, gynocological and emergency health services by both public vehicle and private transit. Results Individuals in the urban core had the highest multimodal accessibility, because of general proximity to the one central public transit hub. While hospitals are centrally accessible, and all pediatricians, 92 percent of OBGYNs, and 88 percent of family medical locations are transit accessible only 53 percent of pregnant women did not live within 400 m of the bus line. A travel time matrix for this data will be presented at the conference. Several specific areas of concern were identified, such as areas outside the urban core, along a rural continuum which is punctuated by low socioeconomic enclaves of high density, low-income housing (trailer parks). Analysis of Medicaid status and maternal health issues is ongoing. Conclusions Accessibility maps at the census block level have been distributed to community stakeholders. Further analysis of rural health in trailer park communities is imperative. This study has implications for Kalamazoo County health, but also the health of other urban-rural counties across the country with similar vulnerable populations.

Dynamic Routing: Anticipation of Emission-Sensitive Traffic Management

Abstract: Delivery companies are affected by emission-sensitive traffic management systems. These systems are installed in cities to react instantly to emission hot-spots through adapting traffic light programs at intersections. This results in a change of the travel times for the delivery vehicle. In this paper, we model the problem in a dynamic vehicle routing problem with stochastic transition of deterministic travel time matrices. To solve this problem, we apply approximate value iteration, a method of approximate dynamic programming, to anticipate future travel time matrix changes in dynamic routing decisions. We vary the approach in the level of information about the state of the traffic management system. This allows to distinguish the required information for the routing decisions. Further, we compare the dynamic and anticipatory routing policies with a static a priori routing. Computational studies show an improved tour duration of routing with traffic management information over the a priori routing by up to 6.5%. We further show that an efficient representation of the traffic management system status in the approximate value iteration approach is mandatory to achieve sufficient anticipation.

Traffic Analysis and OD Travel Time Matrix Based on Two-Fluid Model

The traditional four-step traffic demand forecast model is limited by the long processing time and high cost of origin destination (OD) matrix investigation. A dynamic traffic assignment model is difficult to establish for a large-scale network. To obtain the traffic character and quickly search the shortest path of different zones for dynamic route guidance, the authors analyze travel distance and travel time using taxi global positioning system (GPS) data, fit the travel time and stop time relation curve, and propose a method of traffic analysis using the two-fluid curve. The authors find that the bandwidth of the two-fluid curves is valuable for traffic operation and guidance. Subsequently, the authors analyze the relationship between unit distance travel time and unit distance stop time of different zones. The results verify that the sensitivity of different traffic zones varies. Finally, matrix iteration is used to calculate the shortest travel time path under different unit distance stop times, and the OD travel time matrix is analyzed. The findings indicate that the two-fluid method can be used for dynamic route guidance.

Applications of network analysis and multi-objective genetic algorithm for selecting optimal water quality sensor locations in water distribution networks

In this study, optimal water quality sensor placement is performed based on the sensitivity of flow direction under different water demands for detecting accidental water quality contamination. First, Betweenness Centrality (BC), a network analysis method, is used for determining optimal locations considering a network's connectivity. Second, sensor locations are optimized for minimizing the contaminant intrusion detection time using the travel time matrix and the Multi-Objective Genetic Algorithm (MOGA). These methods were applied to two water distribution networks. It was found that the BC method generates optimal locations close to the water sources and the water main, whereas the MOGA-based method generates optimal sensor locations far away from the sources. These results support the following conclusions. First, the installation priority of gauges can be determined with a more objective standard using the aforementioned two methods. Second, given specific objectives, the two models can be used as alternative decision-making tools for sensor installation.

A spatio-temporal modelling approach for the study of the connectivity and accessibility of the Guangzhou metropolitan network

The urban growth of large cities in China is at a critical stage with the booming of the economy and impressive increase of the population and traffic demand. This paper studies and qualifies the growth and accessibility of a rapid rail transit network, and characterizes the relations with urban development using a spatio-temporal modelling approach. Several measures of the network topological structure, i.e., beta index (β), cyclomatic number (μ), alpha index (α) and gamma index (γ), are selected in order to examine and quantify the overall metro network growth of the city of Guangzhou in China. The results show that the current spatial connectivity of the Guangzhou’s metro network is relatively low, this stressing the need to augment thereliability ofthe connections between the network nodes, and to increase the number of circuits in the network. A travel-time matrix is modelled and evaluates the nodes accessibility and characterizes the spatio-temporal evolution of the metro network. The spatial interaction between the different nodes of the network, as well as nodes accessibility are analyzed and derived from a potential-based model. The extension of the metro network clearly shows a dramatic tendency of positive accessibility evolution but with regional differences. In particular, the core of the city is surrounded by areas with highest accessibility values and gradually expanding outward from the core, while the locations of transfer stations have significant influence on the variation of network time-based accessibility. Taking into account different network development scenarios, the approach reveals regional accessibility differences in the metropolitan area of the city of Guangzhou, this clearly illustrating the impact of network accessibility in urban development.

Accessibility Futures

AbstractThis study uses accessibility as a performance measure to evaluate a matrix of future land use and network scenarios for planning purposes. The concept of accessibility dates to the 1950s, but this type of application to transportation planning is new. Previous research has established the coevolution of transportation and land use, demonstrated the dependence of accessibility on both, and made the case for the use of accessibility measures as a planning tool. This study builds off of these findings by demonstrating the use of accessibility‐based performance measures in the Twin Cities Metropolitan Area. This choice of performance measure also allows for transit and highway networks to be compared side‐by‐side. For roadway modeling, zone‐to‐zone travel time matrix was computed using stochastic user equilibrium (SUE) assignment with travel time feedback to trip distribution. A database of schedules was used on the transit networks to assign transit routes. This travel time data was joined with the land use data from each scenario to obtain the employment, population, and labor accessibility from each traffic analysis zone (TAZ) within specified time ranges. Tables of person‐weighted accessibility were computed for 20 minutes with zone population as the weight for employment accessibility and zone employment as the weight for population and labor accessibility. Maps of accessibility by zone were produced to show the spatial distribution of accessibility across the region. The results show that a scenario where population and employment growth are concentrated in the center of the metropolitan area would produce the highest accessibility no matter which transportation network changes are made. However, another scenario which concentrates population growth in the center of the metropolitan area and shifts employment growth to the periphery consistently outperforms the scenario representing the projected 2030 land use without any growth management strategy.

Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications

Many spatial analysis tasks call for the use of travel time between multiple origins and destinations, that is, O–D travel time matrix. Commercial geographical information systems (GIS) software requires the input of a well-defined road network dataset and significant efforts in implementing the task. However, road network data are often outdated, miss critical road condition details, or are expensive to acquire; and skillful usage of related software is a major obstacle for researchers without advanced training in GIS. This research develops a desktop tool for implementing the task by calling the Google Maps Application Programming Interface (API). By doing so, we are able to tap into the dynamically updated transportation network data and the routing rules maintained by Google and obtain a reliable estimate of O–D travel time matrix. The results are compared with those computed by the ArcGIS Network Analyst module to demonstrate its advantages. A case study in accessibility analysis is presented to illustrate the implications.

Estimating time dependent O‐D trip tables during peak periods

AbstractIntelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real‐time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin‐destination (O‐D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O‐D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O‐D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding‐off, and, 2) low ratio of total number of trips to total number of O‐D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O‐D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed.

Quantitative geographie als mittel zur lösung von planerischen umweltproblemen

Quantitative Geography is a new way of doing research in Geography. It Is based on the philosophy of science and relies heavily on mathematical methods, statistical techniques, and logical systems. The adjective “Quantitative” is misleading insofar as analysis may not be restricted to quantitative data, but may include qualitative data as well. It aims at finding theories on spatial processes, modelling spatial distributions and generally discovering spatial order. These are concepts of great value to present day spatial planning problems. The paper discusses the application of factor analysis in constructing “suitability maps”, first in an example for settlement and cultivation planning in Kenya, then in a model for a geographical explanation of the process of abandoning cultivated land by farmers in a region of the Swiss Alps. The relevance of purely spatial criteria is pertinent (67.5 % explained). Then the notion of “relative space” is introduced and its relevance for planning purposes is discussed. A method for constructing non-centered “relative space” maps (Trilateration, Smallest Space Analysis) is described and some examples from the area around Zürich are developed (with a travel time matrix, a travel cost matrix, and a road distance matrix). Together with a computed centrality index, these maps appear useful for evaluations with regard to outdoor recreation planning etc.