Traditional approaches to travel behaviour modelling primarily rely on household travel survey data, which is expensive to collect, resulting in small sample sizes and infrequent updates. Furthermore, such data is prone to reporting errors which can lead to biased parameter estimates and subsequently incorrect predictions. On the other hand, mobile phone call detail records (CDRs), which report the timestamped locations of mobile communication events, have been successfully used in the context of generating travel patterns. However, due to their anonymous nature, such records have not been widely used in developing mathematical models establishing the relationship between the observed travel behaviour and influencing factors such as the attributes of the alternatives and the decision makers. In this paper, we propose a joint modelling framework that utilises the advantages offered by both travel survey data and low-cost CDR data to optimise the prediction capacity of traditional trip generation models. In this regard, we develop a model that jointly explains the reported trips for each individual in the household survey data and ensures that the aggregated zonal trip productions are close to those derived from CDR data. This framework is tested using data from Dhaka. Bangladesh consisting of household survey data (65,419 persons in 16,750 households), mobile phone CDR data (over 600 million records generated by 6.9 million users), and aggregate census data. The model results show that the proposed framework improves the spatial and temporal transferability of the joint models over the base model which relies on household travel survey data alone. This serves as a proof-of-concept that augmenting travel survey data with mobile phone data holds significant promise for the travel behaviour modelling community, not only by saving the cost of data collection, but also improving the prediction capability of the models.
Transit signal priority (TSP) and freight signal priority (FSP) allow transportation agencies to prioritize signal service allocations considering the priority of vehicles and, potentially, decrease the impact signal control has on them. However, there have been no studies to develop guidelines for implementing signal control considering both TSP and FSP. This paper reports on a study conducted to provide such guidelines that employed a literature review, a simulation study, and a decision tree algorithm based on the simulation results. The guideline developed provides recommendations in accordance with the signal timing slack time, the proportion of major to minor street hourly volume, hourly truck volume per lane for the major street, hourly truck volume per lane for the minor street, the proportion of major to minor street hourly truck volume, the proportion of major to minor street hourly bus volume, the volume-to-capacity ratio for the major street, and the volume-to-capacity ratio for the minor street. The guideline developed was validated by implementing it for a case study facility. The validation result showed that the guideline works correctly for both high and low traffic demand.
Connected vehicle (CV) technologies are expected to have a significant influence on the investment decisions of transportation system management and operations (TSMO) in the near future. One of the potential applications is the use of CV data to support various TSMO processes. This study investigates the use of CV data as an alternative to existing data acquisition techniques in providing two critical functions to support TSMO: travel time estimation and incident detection. In support of this investigation, the study develops regression models to estimate the accuracy and reliability of travel time measurement and latency of incident detection as functions of the traffic demand level and the proportion of CV in the traffic stream. The developed regression models are used in conjunction with a prediction of CV proportions in future years to determine when the CV technology can provide sufficient data quality to replace existing data acquisition systems. The results can be used by TSMO programs and agencies to plan their investment in data acquisition alternatives in future years.
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