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This paper presents a new method to quantify the potential user time savings if the urban bus is given preferential treatment, changing from mixed traffic to an exclusive bus lane, using a big data approach. The main advantage of the proposal is the use of the high amount of information that is automatically collected by sensors and management systems in many different situations with a high degree of spatial and temporal detail. These data allow ready adjustment of calculations to the specific reality measured in each case. In this way, we propose a novel methodology of general application to estimate the potential passenger savings instead of using simulation or analytical methods already present in the literature. For that purpose, in the first place, a travel time prediction model per vehicle trip has been developed. It has been calibrated and validated with a historical series of observations in real-world situations. This model is based on multiple linear regression. The estimated bus delay is obtained by comparing the estimated bus travel time with the bus travel time under free-flow conditions. Finally, estimated bus passenger time savings would be obtained if an exclusive bus lane had been implemented. An estimation of the passenger's route in each vehicle trip is considered to avoid average value simplifications in this calculation. A case study is conducted in A Coruña, Spain, to prove the methodology's applicability. The results showed that 18.7% of the analyzed bus trips underwent a delay exceeding 3min in a 2,448m long corridor, and more than 33,000h per year could have been saved with an exclusive bus lane. Understanding the impact of different factors on transit and the benefits of a priority bus system on passengers can help city councils and transit agencies to know which investments to prioritize given their limited budget., Author(s): Yaiza Montero-Lamas (corresponding author) [1]; Margarita Novales [1]; Alfonso Orro [1]; Graham Currie [2] 1. Introduction Improving bus systems to attract new users is essential to achieving more sustainable [...]

Value of Time (VOT) is a key element in transport infrastructure planning decisions. It is widely believed that travellers using autonomous vehicles (AVs) will have a lower VOT than conventional vehicles (CVs) because of improved road safety, increased road capacity, engagement in productive activity during travel and increased mobility. A number of researchers have measured VOT effects of AVs though no consolidation of this evidence has been undertaken to date. This is the first study to provide a quantitative summary and synthesis of the estimated VOT of AV travel including a synthesis of factors affecting AV VOT. Meta-analyses of 24 studies show that mean VOTs for conventional cars, private AVs and shared AVs are respectively 17.3, 12.1 and 19.4 AU$/hr. However, wide variations in these estimates are evident and 12 factors have been identified with statistically significant effects on these variations including geographical context, study experimental characteristics, trip characteristics and socio-economic factors. We found that AV VOT estimates are lower when animated videos are used to demonstrate AV capabilities. Private AVs have the lowest VOT estimates as do commute trips and morning peak periods. Young adults (18–30 years) and people with current driving experience consistently report lower AV VOT while those with higher educational status and higher-incomes report having higher AV VOT. Implications of findings for policy and future research are presented. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles (AV) is expected to significantly impact the built environment in the long-term. However, the mechanism through which these effects would occur is not known. This study aims to develop conceptual frameworks in the form of causal loop diagrams to enhance understanding through a systematic scoping review of the literature. The review process followed the PRISMA framework and 82 eligible studies were sourced from the Scopus and Web of Science databases. Data were extracted for six attributes of the built environment (parking, density, land use diversity, destination accessibility, urban sprawl and street design). Both qualitative/speculative and quantitative findings are presented stratified by AV types (i.e. shared-autonomous vehicle and private autonomous vehicles), and geographical contexts (i.e. citywide, suburbs and central business district). The findings show that the long-term effects of AVs on the built environment would not be uniformly distributed across the city and vary by AV types. Built environment effects would occur through changes in accessibility, the redistributive demand for parking spaces and other mechanisms. The study provides a knowledge repository and identifies gaps in knowledge for researchers and practitioners interested in the long-term effects of AVs on the built environment. [ABSTRACT FROM AUTHOR]