Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study

Anta, Javier; Pérez-López, José B.; Martínez-Pardo, Ana; Novales, Margarita; Orro, Alfonso

doi:10.1007/s11116-015-9578-1

Cited by 24 publications

(8 citation statements)

References 24 publications

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“…Most studies take precipitation (presence and intensity of rain, snow, and hail) as the most important factor. Temperature and wind are considered less important in terms of individual's behaviour [36]; however, these have a significant impact on thermal comfort. Insolation, cloud cover, air pressure, and humidity are rarely examined [1].…”

Section: Applied Modelling Proceduresmentioning

confidence: 99%

“…In recent years the studies about weather impact on travel demand have been carried out in the European countries with climatic conditions significantly different from Northern Europe, like Austria, Germany, Serbia, and Spain. Most of these papers, except Anta et al [36], have not been cited in papers dealing with weather impact on travel demand, thus indicating that insufficient attention is paid to research studies in these parts of Europe. The statement that it is important to explore the weather impact on travel demand in different climate areas is confirmed by several published papers described below.…”

Section: Trip Generation Modellingmentioning

confidence: 99%

“…It was shown that temperature, sunshine hours, and fresh snowfall had the highest impact, while precipitation and snow depth had less impact on the traffic demand during the winter. During the summer, the temperature and [44] Binomial logit GEE model Petrović 2017 [34] Generalised structural equation models Böcker et al [22] Factor analysis; cluster analysis; regression models Clifton et al [2] Mixed logit model Rudloff et al [9] MNL-GEE regression model Cools and Creemers [47], Creemers et al [37] Multinomial logit model Sabir et al [41,Sabir [19], Saneinejad [43], Saneinejad et al [42], Böcker et al [21], Böcker [55], Liu et al [18], Anta et al [36] Ordered probit model Khattak and De Palma [45] Quantal response model Aaheim and Hauge [7] Note: MNL -Multinomial logit which indicate the need to investigate this subject in more detail. For the prediction of travel demand in order to create a resilient public transport system, several models have been developed.…”

Section: Trip Generation Modellingmentioning

confidence: 99%

“…The influence of weather on the motorized modes of travel (bus, rail, and passenger car, as possible alternatives) was investigated on one of the most important corridors in Barcelona (Spain). Anta et al [36] concluded that the travellers preferred to use rail compared to the bus in adverse weather conditions. On the other hand, bus was more likely to be used than a passenger car, because of bus priority on some sections of the corridor.…”

Section: Disscusion and Conclusionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

Petrović¹,

Ivanović²,

Djoric³

et al. 2020

PROMET

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This paper presents an overview of the applied research methodologies and developed travel demand models that take weather impact into account. The paper deals with trip generation and modal split as elements of travel demand that best describe changes in the travel behaviour in different weather conditions. The authors herein emphasize the importance of research in local conditions in all climate zones, especially in areas where climate and modal split characteristics are different from those in common research areas. This review is designed as a brief guide on how the impact of weather can be explored in order to encourage conducting research even in the countries where there is no systematic traffic and travel data collection. The stated adaptation technique followed by the panel household travel surveys may be particularly appropriate for those countries. It is concluded that small budgets should not be considered an obstacle, because it is possible to draw reliable conclusions based even on small samples. Moreover, modern research methods enable a cheaper survey process together with the possibility of obtaining higher quality of results. The increasing popularity of research in this field should contribute to the creation of more resilient transport systems all over the world. A special contribution of this paper is the review of research studies carried out in central, western and southern Europe and not mentioned in any review paper before.

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Section: Applied Modelling Proceduresmentioning

confidence: 99%

Section: Trip Generation Modellingmentioning

confidence: 99%

Section: Trip Generation Modellingmentioning

confidence: 99%

Section: Disscusion and Conclusionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

Petrović¹,

Ivanović²,

Djoric³

et al. 2020

PROMET

View full text Add to dashboard Cite

show abstract

“…Previous research has presented several models of traffic situation awareness and alerting by constructing various kinds of ITSs. Many state-of-the-art models, including, but not limited to, deep learning [15] and wavelet-supported vector machines [16], have been utilized to estimate and report car crashes or accidents [17], roadside sulfur dioxide and nitrogen oxide data projections [18], identification of suspicious unlicensed taxis [19], the choice of transport mode [20], etc.…”

Section: Traffic Situation Awareness Models In Itssmentioning

confidence: 99%

Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

et al. 2018

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Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.

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Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

2022

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In the face of a continuous increase in the number of tourists in the Alps, the associated traffic volumes, and the resulting negative externalities, there is an urgent need to design policies capable of managing tourist traffic efficiently and to invest in transport systems and infrastructure wisely, given the limited financial, spatial and environmental resources. Unfortunately, while there is a considerable research interest in long-distance travel and arrival/departure patterns of tourists, research on tourist mobility during the stay at the destination is almost non-existent. This prevents policy-makers from making informed decisions backed by scientific evidence. The paper presents a data collection study that contributes to filing this gap. This ‘data paper’ reports on the motivation, design and administration of a bespoke travel-activity survey of tourists at vacation destinations. It informs other researchers about the encountered difficulties and helps them optimize data collection effort in their future studies. Besides the contribution to survey methodology, the paper reports on a rare and policy-relevant dataset, provides descriptive results, and thereby contributes to evidence-based design of transport policies in tourist regions. With this paper, we also want to inspire and invite researchers to conduct further quantitative and modeling work in this under-studied field.

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Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study

Cited by 24 publications

References 24 publications

Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

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