Car Travel Time Research Articles

The experience of transportation to visit a nursing home resident: A case study

ABSTRACT Maintaining social connections in the community can be accomplished through transportation access and opportunities. This includes relationships between family and residents in nursing homes. Previous research supports the relationship between transportation, social support, and visitation of residents in nursing homes (NHs), however no empirical research to date explores this relationship of family member transportation as a means to visit their loved one in this setting. Guided by a case study approach, a sample of 11 (N = 11) family members of residents in nursing homes across North Central Texas were selected to develop an in-depth understanding of transportation access and mobility, as it relates to visitation of residents in nursing facilities. Analyses revealed the following seven themes: Car access, Alternative modes, Flexibility, Travel time, Actual cost, Collateral cost, and Health and Mobility. Findings from this study uncover how cost, both actual cost and collateral cost, are greatly linked to car access, transportation access, and opportunities to visit, as well as the impact each of these features, and emergent themes, related to transportation have on maintained or fractured relationships of family and residents in nursing homes. This article concludes with implications for future research and social work practice.

Identification of contributing factors on travel mode choice among different resident types with bike‐sharing as an alternative

This study mainly studies the contributing factors on residents’ travel mode choices after the emergence of bike-sharing. In contrast to existing studies, the authors divided the travellers into commuters, students, and other travellers by travel purposes, and analysed their travel mode choice by a mix logit model, respectively. It is found that the factors on residents’ travel mode choices have many similarities and differences. Gender, private car ownership, travel cost, travel distance, and travel time are the common factors for all travellers; economy and comfort preference are the factors that affect commuters and students; commuters and other travellers are affected by age, income, and safety preference. However, occupation and an environmental preference are unique significant factors on commuters; students are affected by owning a bike; and a good understanding of bike-sharing is the only significant factor that affects other travellers. In addition, comfort preference has a significant negative influence on the choice of public transport and bike-sharing for students, while it has a significant positive impact on the choice of a private car for commuters.

A multi-decade longitudinal analysis of transportation and land use co-evolution in the Greater Toronto-Hamilton Area

Transportation infrastructure networks (TINs), and the accessibility that they provide, are argued to be major determinants of land-use change. However, the spatio-temporal extent of their impact on urbanization is unclear and calls for further investigation. This paper empirically investigates the impact of both road and transit networks on urbanization in the Greater Toronto-Hamilton Area (GTHA) from 1971 to 2016, using a traffic analysis zone (TAZ) level panel database of 17,160 observations. It first documents and measures the magnitude, location and rate of urbanization and TINs' development in the region and in relation to each other using descriptive and buffer analyses. It then estimates the share of transport accessibility in urbanization over time and space using fixed effects panel regression models. Our findings demonstrate the significant extent of the ongoing sprawl, the substantial historical and current difference between the potential transit and car travel times, and the stability of the potential car travel times despite improvements to the road network. They also highlight the importance of urban proximity in prompting urbanization and the decreasing impact of transit improvement on urban development in urban areas and over time. A policy implication is that to encourage transit-oriented development, it is more effective to introduce or reinforce connections to activity centers rather than invest in reducing travel times to reach them.

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston.

Disparities in travel times between car and transit: Spatiotemporal patterns in cities

Cities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: R < 1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.

A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area

This paper presents a closed-form Latent Class Model (LCM) of joint mode and departure time choices. The proposed LCM offers compound substitution patterns between the two choices. The class-specific choice models are of two opposing nesting structures, each of which provides expected maximum utility feedback to the corresponding class membership model. Such feedback allows switching class membership in response to the changes in choice contexts. The model is used for an empirical investigation of commuting mode and departure time choices in the Greater Toronto and Hamilton Area (GTHA) by using a large sample household travel survey dataset. The empirical model reveals that overall 38% of the commuters in the GTHA are more likely to switch modes than departure times and 62% of them are more likely to do the reverse. The empirical model also reveals that the average Subjective Value of Travel Time Savings (SVTTS) of the commuters in the GTHA can be as low as 3 dollars if a single choice pattern of departure time choices nested within mode choices is considered. It can also be as high as 67 dollars if the opposite nesting structure is assumed. However, the LCM estimates the average SVTTS to be around 27 dollars in the GTHA. An empirical scenario analysis by using the estimated model indicates that a 50% increase in morning peak period car travel time does not sway more than 4% of commuters from the morning peak period.

Controlling Signalized Intersections using Machine Learning

Signalized intersections are the capacity-determining points on roads in cities, and the signal settings are usually based on very primitive algorithms which cause road users to experience a lot of unnecessary delay The work presented in this paper, show the effect of deploying a controller based on the optimization software Uppaal Stratego in four signalized intersections on the same road segment, the controller is fully distributed meaning there is no direct coordination between the intersections. The controller is tested against the controller deployed in the intersections today. The controllers have been tested using the micro simulation program VISSIM. The simulation shows that in comparison with the existing controller, this controller provides a reduction of between 30% and 50% in average delays, queues and number of stops. The fuel consumption and total travel time of cars in the coordinated section are reduced by about 20% in the simulation study. All these reductions are achieved without making the situation worse for the side roads.

Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors

Travel time information is an integral part in various ITS applications such as Advanced Traveler Information System, Advanced Traffic Management Systems etc. Travel time data can be collected manually or by using advanced sensors. In this study, suitability of Bluetooth and RFID (Radio Frequency Identifier) sensors for data collection under mixed traffic conditions as prevailing in India is explored. Reliability analysis was carried out using Cumulative Frequency Diagrams (CFDs) and buffer time index along with evaluation of penetration rate and match rate of RFID and Bluetooth sensors. Further, travel time of cars for a subsequent week was predicted using the travel time data obtained from RFID sensors for the present week as input in ARIMA modeling method. For predicting the travel time of different vehicle categories, relationships were framed between travel time of different vehicle categories and travel time of cars determined from RFID sensors. The stream travel time was then determined considering the travel time of all vehicle categories. The R-Square and MAPE values were used as performance measure for checking the accuracy of the developed models and were closer to one and lower respectively, indicating the suitability of the RFID sensors for travel time prediction under mixed traffic conditions. The developed estimation schemes can be used as part of travel time information applications in real time Intelligent Transportation System (ITS) implementations.

Assessment of the spatial accessibility to health professionals at French census block level

Abstract Background The evaluation of geographical healthcare accessibility in residential areas provides crucial information to public policy. This work developed an improved indicator: the Index of Spatial Accessibility (ISA) to measure geographical healthcare accessibility at the smallest available infra-urban level, that is, the Îlot Regroupé pour des Indicateurs Statistiques. Methods This study was carried out in the department of Nord, France. Healthcare professionals are geolocalized using postal addresses available on the French state health insurance website. ISA is derived from Enhanced Two-Step Floating Catchment Area (E2FCA) algorithm. We have constructed a catchment for each healthcare provider, by taking into account residential building centroids, car travel time as calculated by Google Maps and the edge effect. Principal Component Analyses (PCA) were used to build a composite ISA to describe the global accessibility of different kinds of health professionals. Results We applied our method to studying geographical healthcare accessibility for pregnant women, by selecting three types of healthcare provider: general practitioners, gynecologists and midwives. A total of 3587 healthcare providers are potentially able to provide care for inhabitants of the department of Nord. On average there are 92 general practitioners, 22 midwives and 21 gynecologists per 100,000 residents. The composite ISA for the three types of healthcare provider is 39 per 100,000 residents. Conclusions ISA is a multidimensional and improved measure, which combines the volume of services relative to population size with the proximity of services relative to the population’s location, available at the smallest feasible geographical scale. It could guide policy makers towards highlighting critical areas in need of more healthcare providers, and these areas should be earmarked for further knowledge-based policy making. Key messages Indicator of healthcare access at fine spatial scale allows us to identify precisely critical areas where healthcare professionals need to be allocated. ISA is a multidimensional measure, which combines the volume of services relative to population size with the proximity of services relative to the population’s location.

Ann Modeling For Predicting Car Travel Time using Bus As Probe.

The critical issue of Intelligent Transportation Systems (ITS) applications is obtaining the near real time information of travel times. This paper proposes a dependable model for predicting car travel time on urban roads in Greater Cairo using buses as probes. The GPS receivers, which are installed on test vehicles and buses, used to collect real travel time data along the urban roads. The travel times of bus and car are compared in order to recognize similarities and differences between the trip profiles of test vehicles and buses. According to the comparison results, the model is developed and validated using Artificial Neural Network (ANN) for estimating car travel time using buses’ travel time with acceptable level of accuracy equals 10.53%.

Research of the possibilities to increase the traffic capacity of streets in the central part of Irkutsk

The article presents the results of the research of the travel time of private cars and route passenger vehicles, depending on the presence or absence of cars parked along the street. The research is conducted in one of the busiest streets located in the central part of Irkutsk, where one-way traffic is organized. The increase in the level of automobilization and the development of the historic part of the city resulted in a sharp increase in the number of parked along the street cars. This situation caused a significant decrease in speed and, accordingly, an increase in the time spent on a trip. As a result of the research, it has been established that restricting or even a complete ban on parking (and corresponding control over its execution) in the busiest part of the street will increase significantly its traffic capacity and travelling speed of either private cars or route passenger vehicles.

Max-plus algebraic modeling of three crossroad traffic queue systems with one underpass

This research aims to model traffic flow queuing system of three crossroads, namely the Monjali, Kentungan, and Gejayan crossroads in Yogyakarta with one underpass at the Kentungan crossroad using max-plus algebra. This research is based on data of traffic flow density, especially cars and traffic light duration data on the three crossroads. Then, a crossing condition scheme is created and represents the direction of car movement especially in the direction from Monjali to Gejayan. Then the model is compiled which includes the waiting time of cars when the red traffic light is on, the time cars leave the crossroad during the green traffic light is on, and the travel time of cars between crossroad with max-plus algebra, so that the total vehicle time can be determined from Monjali to the Gejayan crossroad . The results show that the queueing system can be modelled using max-plus algebra. Furthermore, it can be determined the waiting time, the time to leave crossroad, and the total time needed to cross from the Monjali crossroad to exit the Gejayan crossroad. The resulting model can reduce congestion on the lines of the intersection of Monjali, Kentungan and Gejayan.

Time-space Visualization of Automobile and Airplane Travel Time from Chicago to Various Destination Cities

Abstract. Travel distances are often thought of in terms of time. When asked about how far away a destination is, we answer that it is “x” minutes or hours away. This study explores a novel approach to visualize travel time from a selected, specific city to various destination cities in the Conterminous United States using two modes of transportation: automobile and airplane. Each destination city was selected randomly from cities that met a set of criteria including: city population categories, state representation, and overall geographic distribution. For the automobile and airplane time-space visualization Google Maps were consulted to get driving time and flying time estimates to 107 destination cities. For the airplane travel additional time was added to estimate driving to a local airport and parking. A straight-line time-vector trajectory between the example start city, Chicago, and the destination cities provided a consistent line to alter the relative locations of destination cities relative to buffer distances representing time. Time-altered city locations were used as control points to align an existing map of the United States. The automobile results show pronounced geographic dilation as well as contraction along many coastal states. Airplane travel severely contracted geographic extent of the U.S. as flying time is much faster and far less variable than automobile time. These visualizations provided a different cartographic product that emphasizes the time and space perception changes in time and space associated with movement via different modes of travel.

Accessibility Impacts of Park-and-Ride Systems

This work presents a methodology for calculating park-and-ride (PNR) accessibility and provides case study results for the Minneapolis–Saint Paul, Minnesota (Twin Cities) facility system. PNR is a form of mixed-mode transit travel which is studied for its impacts on access to opportunities. Regional PNR systems offer a long-standing and widespread example of the collective benefits of mixed-mode travel. The Twin Cities metropolitan region has over 100 PNR facilities that are primarily connected to business districts through express and limited-stop transit service. PNR trip types require automobile and transit travel time matrices to link across space and time to capture mixed-mode travel characteristics. The resulting matrix is used in a cumulative accessibility analysis in which total jobs accessible within a travel time threshold is the variable of interest. Experimental results indicate that PNR facilities affect the suburban transit accessibility profile more than exurban or urban areas during the morning commute. The average worker-weighted job accessibility for a 30-min PNR trip increases by 230% from the comparable walk-to-transit measure. The transit accessibility made available through PNR facilities highlights the need to include PNR trip types in transit accessibility analyses and suggests that current methods underestimate transit accessibility in suburban regions.

Rational choice in everyday mobility decisions of Austrian employees

ABSTRACTBoth, the capability of a transport system to grant access to a destination and a traveller’s ability to utilize its opportunities can be distinguished in objective and subjective factors. The paper analyses the potential for objective accessibility factors to explain mobility behaviour. In more detail, the paper (i) quantifies the explanatory power of objective factors of the transport system (supply-side) for mode choice decisions and (ii) analyses if these supply-side factors, as well as objective factors of the traveller (demand-side), affects the level of multimodality among Austrian employees. Following this approach, the paper estimates mode choice models that only consist of trip features and accessibility factors. The final model reaches a high explanatory power and includes the individual transport mode availability, travel time, travel cost, public transport service quality and the car parking situation. The model allows simulation, which estimates the modal shift with respect to adaptations of the accessibility factors. Car travel time is the variable impacting mode choice decisions the most; this suggests a push-strategy when aiming to make the transport system more sustainable. Additionally, we show that both transport demand and supply factors, but most of all their combination can explain multimodal travel behaviour, i.e., whether or not a person uses more than one transport mode within one week. The explanatory value increases significantly if one also considers mobility tools. The variables explaining multimodality best are ownership of a bike and a PT season card as well as the public transport network density.

Modeling the choice to switch from traditional modes to ridesourcing services for social/recreational trips in Lebanon

This study investigates the current and potential uptake of ridesourcing services, such as Uber and Careem, by the students of the American University of Beirut, Lebanon. A hybrid choice model is developed to predict the switching choice from traditional modes of transport to ridesourcing services for social/recreational trips made by these students in Lebanon. Data are provided by a web-based survey that includes revealed and stated preferences, besides demographics. It is found that the switching choice is determined by several observed factors, such as door-to-door travel time, waiting time for pick-up, and one-way fares, in addition to a latent variable that captures individual differences in perceptions and attitudes towards ridesourcing services. A base switching probability from traditional modes to ridesourcing services (calculated under a base scenario representing realistic values of the attributes of ridesourcing services if the latter were used to make the most recent social/recreational trip) is estimated to be 0.22. This probability is expected to reach 0.31 under a forecasted policy scenario consisting of 40% reduction in ridesourcing fares. Car users will be more sensitive to switch to ridesourcing services for their social/recreational trips if the ridesourcing fare reduction (40%) is associated with restricted parking conditions consisting of (a) 100% increase of parking fees from actual prices, and (b) 20-minute increase of parking search time and parking time from the actual car travel time. In this case, the resulting switching probability is expected to reach 0.38. By using the estimated choice model to forecast policy scenarios as such, this study can guide planners, policymakers, and service operators to prioritize effective policies in response to the behavioral change caused by the diffusion of innovative transport services and technologies. The study also contributes to a better understanding of the uptake of ridesourcing services in developing country contexts where public transport services are often inadequate.

The evolution of air transport networks and impacts on shortest travel times between NUTS-3 regions – a case study for intra-European trips originating in Germany

The European air transport market has evolved over the past decades at an amazing pace. Key developments include the rise of the low cost carriers (LCCs), orginally at regional and military conversion airports and now also at the larger secondary airports and hubs, the withdrawal of network carriers from non-hub routes and a widely dissapperance of classical regional air services with turboprops. We hypothesize that all these developments have had impacts on travel times between European regions and hence of the achievement of the European Commission’s Flightpath 2050 goal that 90% of trips within Europe can be completed within 4 hours. In this conference presentation, we present a methodology to analyse shortest car and air travel times including a combination of both modes for trips between European regions. We then apply our model to three test cases for travel from Germany to Europe on the level of NUTS-3 regions (districts and larger cities) in order to address the following questions: First, we assess for which region pairs and distances combinations of car and scheduled air transport offer travel time savings over trips by car only. Second, we evaluate how the evolution of the European air transport network has changed shortest travel times for trips from each German NUTS-3 region to Europe between 2000 and 2017. Third, we analyse to what extent full interlining between any airline - including low cost carriers - could help to reduce shortest travel times. The latter topic is related to the current trend of low cost carriers offering transfer connections and/or acting as feeders for traditional network airlines.

Substantial rebound effects in urban ridesharing: Simulating travel decisions in Paris, France

This paper investigates how and to what extent changes in user behavior may mitigate the environmental benefits of urban ridesharing, a phenomenon commonly referred to as “rebound effect”. Ridesharing reduces both the individual cost of car travel (through cost splitting) and road travel times (by decreasing congestion). This may trigger a number of behavioral changes among transportation users, including: making less detours to avoid congestion (route choice effect), switching from public transit and active modes to the car (modal shift effect), travelling longer distances (distance effect), and relocating further from the urban center (relocation effect). Taking Paris region as a case study, this research applies an integrated transportation/land-use model to evaluate several ridesharing scenarios and quantify the four rebound effects. The overall rebound effect is found to be substantial, cancelling out from 68 to 77% of CO2 emission reductions and from 52 to 73% of aggregated social benefits (including congestion, air quality, CO2 emissions, noise) expected from ridesharing. This is primarily the result of the modal shift effect, supplemented as ridesharing develops by the distance effect. Although the simplified representation of ridesharing in the baseline model calls for caution regarding these estimates, a sensitivity analysis corroborates the main findings and the prevalence of substantial rebound effects. The paper also investigates to what extent three complementary policies - improving public transit, reducing road capacity or increasing the cost of car travel – might limit the overall rebound effect and thereby maximize the benefits of urban ridesharing.

Influence of pricing on mode choice decision in Jakarta: A random regret minimization model

To reduce traffic congestion, the Government of Jakarta is planning to implement electronic road pricing (ERP) for commuters who pass through arterial roads in Jakarta’s CBD area. This paper studies the implementation of ERP, which in this study is called contribution cost, by utilizing the recently introduced alternative choice model approach called random regret minimization (RRM). In RRM when deciding, individuals are assumed to minimize their anticipated regret as opposed to maximizing their utility. A stated preference (SP) survey has been conducted with 507 respondents. Four alternative modes (public transport, park and ride, car, and motorcycle) were presented with several attributes such as travel time, travel cost, waiting time, transfer, parking cost, and contribution cost. The SP survey was divided into two parts, part 1 used for Model 1 with all attributes including contribution cost, and part 2 used for Model 2 with only travel time and travel cost (without the contribution costs). In total 6003 observations for 12 scenarios in Model 1, and 4011 observations for 8 scenarios in Model 2 were obtained. Comparing model fit and prediction accuracy, Model 2 outperforms Model 1. Regarding the value of travel time savings (VTTS), it appears that the incorporation of contribution cost (and other attributes) results in substantially higher VTTS for Model 1 compared to Model 2. Finally demand elasticities are larger than one for public transport, park and ride and car travel time. The results also show that car contribution cost elasticity is substantially higher than motorcycle contribution cost elasticity.

Telehealth model of care for routine follow up of renal transplant recipients in a tertiary centre: A case study

Royal Melbourne Hospital (RMH) performs over 140 kidney transplant operations annually. Kidney transplant recipients require regular medical review, which results in loss of time and costs from travel, particularly for regional patients, and places high demand on the hospital outpatient service. The RMH renal transplant unit initiated a telehealth service in 2016 to provide cost effective, patient-centred clinical care for regional patients. To date, 263 clinical reviews have been conducted via telehealth, potentially saving 203,202 kilometres in travel distance; 2771 hours in car travel time; an estimated AUD $31,048 in petrol savings and 51 tonnes CO2 equivalents of greenhouse gas emissions. Lessons learnt have included the importance of using technology that allows patients to access telehealth from their place of choice. The option of a joint consultation with local healthcare providers has facilitated the development of extended care networks for our patients. Incorporation of telehealth into our outpatient system has been achieved with the existing nephrology workforce, making it a sustainable long-term review option. Our renal transplant telehealth outpatient clinic has been a successful change in the way we provide care to regional patients. Formal comparison of clinical outcomes and the patient experience of telehealth versus in person reviews are underway.