Impact of weather conditions on macroscopic urban travel times

Tsapakis, Ioannis; Cheng, Tao; Bolbol, A

doi:10.1016/j.jtrangeo.2012.11.003

Cited by 110 publications

(68 citation statements)

References 14 publications

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“…Böcker et al (2013) provided an extensive literature review on the potential impacts of weather on individual daily travel behaviors such as trip generation, travel destination and mode choices. Tsapakis et al (2013) showed that high intensity of snow and rain decreases travel speed and increases travel time in the Greater London area. They also found that the impacts of weather conditions largely depend on drivers' attitudes, socioeconomic characteristics and other contextual factors.…”

Section: Introductionmentioning

confidence: 99%

MobRISK: a model for assessing the exposure of road users to flash flood events

Shabou

Ruin

Lutoff

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Recent flash flood impact studies highlight that road networks are often disrupted due to adverse weather and flash flood events. Road users are thus particularly exposed to road flooding during their daily mobility. Previous exposure studies, however, do not take into consideration population mobility. Recent advances in transportation research provide an appropriate framework for simulating individual travelactivity patterns using an activity-based approach. These activity-based mobility models enable the prediction of the sequence of activities performed by individuals and locating them with a high spatial-temporal resolution. This paper describes the development of the MobRISK microsimulation system: a model for assessing the exposure of road users to extreme hydrometeorological events. MobRISK aims at providing an accurate spatiotemporal exposure assessment by integrating travel-activity behaviors and mobility adaptation with respect to weather disruptions. The model is applied in a flash-flood-prone area in southern France to assess motorists' exposure to the September 2002 flash flood event. The results show that risk of flooding mainly occurs in principal road links with considerable traffic load. However, a lag time between the timing of the road submersion and persons crossing these roads contributes to reducing the potential vehicle-related fatal accidents. It is also found that sociodemographic variables have a significant effect on individual exposure. Thus, the proposed model demonstrates the benefits of considering spatiotemporal dynamics of population exposure to flash floods and presents an important improvement in exposure assessment methods. Such improved characterization of road user exposures can present valuable information for flood risk management services.

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Section: Introductionmentioning

confidence: 99%

MobRISK: a model for assessing the exposure of road users to flash flood events

Shabou

Ruin

Lutoff

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…The total travel time increases due to all mild, moderate and heavy rains. Mild snow results in travel time slight increases, whilst heavy snow causes the highest percentage delays (Tsapakis et al, 2013) In our research in order to analyse the effects of weather conditions on schedule, typical days for different weather conditions have been selected in the year of 2012 and 2013. These are summarized in Table 2.…”

Section: Weather Datamentioning

confidence: 99%

Analysis of Delay Causes in Railway Passenger Transportation

Nagy¹,

Csiszár²

2015

Period. Polytech. Transp. Eng.

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Keywords delay causes, rail transport schedule investigation IntroductionPassenger service quality consists of several factors. Researchers applied and validated the Satisfaction with Travel Scale (STS) method that measures the service experience in public transportation. The results confirmed that service experience is multidimensional, consisting of a cognitive dimension related to service quality and two affective dimensions related to positive activation, such as enthusiasm or boredom, and positive deactivation, such as relaxation or stress (Olsson et al., 2012). One of the most important of the quality factors influencing experience is punctuality. Time keeping of schedule is important both on operational and passenger side. The punctuality is not a simple parameter, but a rather complex indicator. According to it both the operational quality of the organization and the technical state of development can be assessed.Passengers plan their journey according to the schedule and the most uncertain element of travel chain is the interchange. On operational side, guaranteeing the connections is a huge challenge, especially in case of infrastructure in bad condition. For these purposes delay prediction is a helpful tool. A study (Corman et al., 2013) proposes a compromise solution for minimization of train delays by comparison of timetable options.Level of service can be modelled by quality loops. The study of the optimal service quality shows that public transport reliability and thereby volume of clientele is often lower at equilibrium compared to first-best social optimum (Monchambert and Palma, 2014). One component of the quality loop is planned quality that is presented to the passengers by the service provider. This quality level depends on the budget, the standards expected by passengers and the performance of competitors. The provided (realized) quality may be varying day by day because of the external factors. Causes of the differences are: factors of the operator (e.g. technical breakdowns) or independent factors (e.g. weather conditions, incidents, accidents). The aim of our research has been determined on basis of literature review: study, analysis and comparison of traffic characteristics of railway lines with different infrastructure (Lindfeldt, 2011) and similar surveys in Norway where 1000 departures identified many reasons for delay (Harris et al., 2013).

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“…There is also some recent literature on the impacts of weather conditions on urban traffic performance (Kamgaand Yazici, 2014;Lam et al, 2013;Tsapakis et al, 2013;Andersen and Torp, 2016). For example, Tsapakis et al (2013) investigated the impact of different intensities of rain on macroscopic travel times in the Greater London area (UK) and found that the ranges of the total travel time increase due to light, moderate and heavy rain are:0.1-2.1%, 1.5-3.8%, and 4.0-6.0% respectively.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Tsapakis et al (2013) investigated the impact of different intensities of rain on macroscopic travel times in the Greater London area (UK) and found that the ranges of the total travel time increase due to light, moderate and heavy rain are:0.1-2.1%, 1.5-3.8%, and 4.0-6.0% respectively. Andersen and Torp (2016) used large-scale GPS data to analyse the correlation between travel time and the weather classes, and they found that snow can increase the travel time up to 27% and strong headwind can increase the travel time with up to 19% (compared to dry calm weather).…”

Section: Introductionmentioning

confidence: 99%

Effects of the Rainstorm on Urban Road Traffic Speed — A Case Study of Shenzhen, China

Hao

Wang

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The adverse weather may significantly impact urban traffic speed and travel time. Understanding the influence of the rainstorm to urban traffic speed is of great importance for traffic management under stormy weather. This study aims to investigate the impact of rainfall intensity on traffic speed in the Shenzhen (China) during the period 1 July 2015 -31 August 2016. The analysis was carried out for five 1-h periods on weekdays during the morning periods (6:00 AM -11:00 AM). Taxi-enabled GPS tracking data obtained from Shenzhen city are used in the analysis. There are several findings in this study. Firstly, nearly half of the roads are significantly affected by the rainstorm. Secondly, the proportion of positive correlated roads is about 35%, but there still are some roads with uncorrelated traffic speed variation rates (SVR) and rainfall intensities. Thirdly, the impact of the rainstorm on traffic speed is not homogeneous but with obvious spatial difference. This research provides useful information that can be used in traffic management on a city-wide scale under stormy weather.

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Impact of weather conditions on macroscopic urban travel times

Cited by 110 publications

References 14 publications

MobRISK: a model for assessing the exposure of road users to flash flood events

MobRISK: a model for assessing the exposure of road users to flash flood events

Analysis of Delay Causes in Railway Passenger Transportation

Effects of the Rainstorm on Urban Road Traffic Speed — A Case Study of Shenzhen, China

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