Spatial and temporal transferability of relationships between travel demand, trip cost and travel time

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

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

Scientific Reports ◽

10.1038/s41598-020-61077-0 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Yuan Liao ◽

Jorge Gil ◽

Rafael H. M. Pereira ◽

Sonia Yeh ◽

Vilhelm Verendel

Keyword(s):

Travel Time ◽

Travel Demand ◽

Time Of Day ◽

Sao Paulo ◽

São Paulo ◽

Real World Data ◽

Car Use ◽

Real Time Traffic ◽

Average Travel Time ◽

Car Travel

AbstractCities 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 $$R$$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: $$R < 1$$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.

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Evaluation of Evacuation Strategies According to the Travel Demand: The Case of Nuclear Research Reactor HANARO’s EPZ

Sustainability ◽

10.3390/su12156128 ◽

2020 ◽

Vol 12 (15) ◽

pp. 6128

Author(s):

Bongseok Kim ◽

Hyeonmyeong Jeon ◽

Bongsoo Son

Keyword(s):

Travel Time ◽

Research Reactor ◽

Travel Demand ◽

Protective Action ◽

Nuclear Research ◽

Emergency Plan ◽

The Public ◽

Nuclear Research Reactor ◽

Total Travel Time ◽

The Republic

In the event of a nuclear accident, evacuation is the most effective protective action for the public. During the evacuation, total travel time is a key measure to protect the public because it is directly related to the public’s radiation exposure. Thus, strategies that reduce the total travel time are needed for a safer nuclear emergency plan. Many studies on evacuation strategies so far have suggested the methodology of effective routing decisions or delay management. Despite the application of those strategies during evacuation, the effectiveness of those strategies, in reality, varies depending on the level of travel demand. In this study, evacuation strategies based on travel demand levels were evaluated based on the case of the Emergency Planning Zone (EPZ) of HANARO, the nuclear research reactor in the Republic of Korea. As a result, it was confirmed that effective evacuation strategies could be applied differently according to travel demand levels.

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Feeder Bus Accessibility Modeling and Evaluation

Sustainability ◽

10.3390/su12218942 ◽

2020 ◽

Vol 12 (21) ◽

pp. 8942

Author(s):

Shixiong Jiang ◽

Wei Guan ◽

Liu Yang ◽

Wenyi Zhang

Keyword(s):

Travel Time ◽

Public Transportation ◽

Analytical Modeling ◽

Travel Demand ◽

Travel Times ◽

Simulation Experiments ◽

Last Mile ◽

Feeder Bus ◽

Travel Services ◽

Demand Responsive Transit

To improve first/last mile travel services between metro stations and communities, this study modeled and analyzed four kinds of feeder bus operation strategies in terms of travel time and accessibility. The analytical modeling was used to compare the travel times and the simulation experiments were used to compare the accessibilities of different operation strategies. The results showed that when the ratio between length and width of study area increases, the number of stops for the fixed route transit with fixed stops will increase. When the travel demand is low, the demand responsive transit with separate routes has the highest accessibility. When the travel demand is high, the fixed route transit with fixed stops provides the highest accessibility. In addition, the ratio of flows in two passenger directions has different influences on the four operation strategies. This study can provide guidance for feeder bus operation to improve public transportation attraction.

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Collective benefits in traffic during mega events via the use of information technologies

Journal of The Royal Society Interface ◽

10.1098/rsif.2016.1041 ◽

2017 ◽

Vol 14 (129) ◽

pp. 20161041 ◽

Cited By ~ 13

Author(s):

Yanyan Xu ◽

Marta C. González

Keyword(s):

Travel Time ◽

Information Technologies ◽

Travel Demand ◽

Collective Good ◽

Large Cities ◽

The Public ◽

The Impact ◽

First Time ◽

Integrate Data ◽

Expected Increase

Information technologies today can inform each of us about the route with the shortest time, but they do not contain incentives to manage travellers such that we all get collective benefits in travel times. To that end we need travel demand estimates and target strategies to reduce the traffic volume from the congested roads during peak hours in a feasible way. During large events, the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for collective benefits in traffic. In this paper, we integrate, for the first time, big data resources to estimate the impact of events on traffic and propose target strategies for collective good at the urban scale. In the context of the Olympic Games in Rio de Janeiro, we first predict the expected increase in traffic. To that end, we integrate data from mobile phones, Airbnb, Waze and transit information, with game schedules and expected attendance in each venue. Next, we evaluate different route choice scenarios for drivers during the peak hours. Finally, we gather information on the trips that contribute the most to the global congestion which could be redirected from vehicles to transit. Interestingly, we show that (i) following new route alternatives during the event with individual shortest times can save more collective travel time than keeping the routine routes used before the event, uncovering the positive value of information technologies during events; (ii) with only a small proportion of people selected from specific areas switching from driving to public transport, the collective travel time can be reduced to a great extent. Results are presented online for evaluation by the public and policymakers ( www.flows-rio2016.com (last accessed 3 September 2017)).

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Impact of Major Road Supply on Individual Travel Time Expenditure: An Exploration with a 30-Year Variation of Infrastructure and Travel

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118791866 ◽

2018 ◽

Vol 2672 (3) ◽

pp. 56-68

Author(s):

Ryosuke Abe ◽

Kay W. Axhausen

Keyword(s):

Travel Time ◽

Large Scale ◽

Travel Demand ◽

Road Traffic ◽

Land Development ◽

Transportation Infrastructure ◽

Major Road ◽

Birth Cohorts ◽

Data Set ◽

The Impact

This study estimates the impact of major road supply on individual travel time expenditures (TTEs) using data that cover 30-year variations in transportation infrastructure and travel behavior. The impacts of the supply of road and rail infrastructure are estimated with a data set that combines records of large-scale household travel surveys in the Tokyo metropolitan area conducted in 1978, 1988, 1998, and 2008. Linear and Tobit models of individual TTEs are estimated by following the behavior of birth cohorts over the 30-year period. The models incorporate the changes in transportation infrastructure, measured as lane kilometers of two levels of major road stock and vehicle kilometers of urban rail service. The results show significant negative effects of lane kilometers for higher-level and lower-level major roads on the TTEs for all travel purposes and for commuting, after controlling for socioeconomic backgrounds and generations of individuals. This study discusses that, in Tokyo, the estimated effect is more likely to reflect the effect of a major road network per se on individual TTEs than the (indirect) effect of major road supply on individual TTEs working through land development activities (i.e., induced car travel demand). For example, the caveat is that actual road investment decisions still need to consider the induced component of road traffic in addition to the (direct) effect that is estimated in this study.

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Designing Limited-Stop Transit Service with Fixed Fleet Size in Peak Hours by Exploiting Transit Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2647-16 ◽

2017 ◽

Vol 2647 (1) ◽

pp. 134-141

Author(s):

Xiaoling Luo ◽

Yangsheng Jiang ◽

Zhihong Yao ◽

Youhua Tang ◽

Yuan Liu

Keyword(s):

Travel Time ◽

Heuristic Algorithm ◽

Travel Demand ◽

Fleet Size ◽

Transit Service ◽

User Costs ◽

Passenger Flow ◽

Line Structure ◽

Total Travel Time ◽

Travel Behaviors

Efficiently designed limited-stop transit service is an attractive way to respond to high commuter travel demand in which trips concentrate on a few origin–destination pairs during peak hours. Such service is redesigned in many metropolises in China. Some research has dealt with this situation; bus fleet size was assumed to be unlimited, and the research was concerned with the average daily passenger flow rather than the specific average peak hour travel demand. In contrast to previous work, this paper presents an approach to design limited-stop transit service with the existing available fleet size from current normal service and focuses only on peak hour travel demand extracted through exploitation of transit data. First, a model for limited-stop service was proposed to minimize user costs through existing fixed fleet size. A heuristic algorithm was developed to search the transit line structure for limited-stop service instead of selecting lines from the predefined set. Next, a case in Chengdu, China, was tested. The results indicate that up to 9.32% of total travel time can be saved with the fixed fleet size when limited-stop transit service is applied. Finally, different proportions of commuter flow and different travel behaviors are discussed to illustrate the performance of limited-stop service for different scenarios.

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Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1854-21 ◽

2003 ◽

Vol 1854 (1) ◽

pp. 189-198 ◽

Cited By ~ 92

Author(s):

Jean Wolf ◽

Marcelo Oliveira ◽

Miriam Thompson

Keyword(s):

Travel Time ◽

Global Positioning System ◽

Travel Demand ◽

Positioning System ◽

Gps Technology ◽

Travel Survey ◽

Household Travel Survey ◽

Time Estimates ◽

Global Positioning ◽

Household Travel

Trip underreporting has long been a problem in household travel surveys because of the self-reporting nature of traditional survey methods. Memory decay, failure to understand or to follow survey instructions, unwillingness to report full details of travel, and simple carelessness have all contributed to the incomplete collection of travel data in self-reporting surveys. Because household trip survey data are the primary input into trip generation models, it has a potentially serious impact on transportation model outputs, such as vehicle miles of travel (VMT) and travel time. Global Positioning System (GPS) technology has been used as a supplement in the collection of personal travel data. Previous studies confirmed the feasibility of applying GPS technology to improve both the accuracy and the completeness of travel data. An analysis of the impact of trip underreporting on modeled VMT and travel times is presented. This analysis compared VMT and travel time estimates with GPS-measured data. These VMT and travel time estimates were derived by the trip assignment module of each region's travel demand model by using the trips reported in computer-assisted telephone inter views. This analysis used a subset of data from the California Statewide Household Travel Survey GPS Study and was made possible through the cooperation of the metropolitan planning organizations of the three study areas (Alameda, Sacramento, and San Diego, California).

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