Group of Railways and Transportation Engineering
University of A Coruña
Yaiza Montero-Lamas, Alfonso Orro, Margarita Novales, Graham Currie, and James Reynolds Elsevier BV
Yaiza Montero-Lamas, Margarita Novales, Alfonso Orro, and Graham Currie Hindawi Limited
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 3 min in a 2,448 m long corridor, and more than 33,000 h 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.
Yaiza Montero-Lamas, Alfonso Orro, Margarita Novales, and Francisco-Alberto Varela-García MDPI AG
This study aimed to investigate the relationship between the characteristics of the areas of influence of bus stops and the decrease in ridership during COVID-19 lockdowns and subsequent initial reopening processes. A novel GIS methodology was developed to determine these characteristics from a large amount of data with high spatial detail and accurately assign them to individual bus stops. After processing the data, several multiple linear regression models were developed to determine the variables related to different activities and changes in mobility during lockdown that may explain the variation in demand owing to the COVID-19 pandemic. The characteristics related to population and land use were also studied. The proposed methodology can be used to improve transit planning during exceptional situations, by strengthening public transport in areas with a predictably higher transit demand, instead of uniformly decreasing the availability of public transport services, promoting sustainable mobility. The efficiency of the proposed methodology was shown by performing a case study that analysed the variation in bus demand in A Coruña, Spain. The areas with the highest sustained demand were those with low inhabitant incomes, a high population density, and significant proportions of land use dedicated to hospitals, offices, or supermarkets.