Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set

Deploying traditional and smartphone app survey methods in measuring door-to-door travel satisfaction in eight European cities

This study describes the lessons learned from designing, deploying and analysing the results from different travel satisfaction survey tools which measures the travellers' door-to-door travel satisfaction. The travel satisfaction measurement survey tools tested consisted of two types of smartphone applications (a satellite navigation app and a game app), an on-line survey, a paper-based semi-structured questionnaire and a focus group questionnaire. Each of the measurement tools comprised the same set of basic questions, but in different formats, aimed at exploring the pros and cons of each tool among different groups of travellers. The data collection was carried out at eight different European cities and five FIA motorist networks. 5,275 valid responses were gathered from the survey. Further analysis results show that different survey methods performed better in different sites. The satisfaction that was gathered via main trip leg does not necessarily correspond with overall satisfaction of the door-to-door journey. The results of this study highlight the need for more inclusive, complete, door-to-door, travel survey measurements

Intrapersonal mode choice variation:Evidence from a four-week smartphone-based travel survey in the Netherlands

This paper examines mode choice variation in the Netherlands based on the trip data of 432 respondents from a four-week smartphone-based travel survey. Trip characteristics, including origin and destination location, arrival and departure time, mode and trip purpose, were automatically recorded, but checked and if necessary revised in a web-based prompted recall survey. Statistical analyses and mixed logit mode choice models were used to explore intrapersonal variation and its effect on mode choice. We found relatively much intrapersonal variation for short trips (<2 km) as respondents who usually travel by car also regularly walk and/or cycle. By contrast, intrapersonal variation was significantly smaller in trips longer than 10 km, suggesting that people choose the same mode when they repeat long journeys. The intrapersonal variation is also relatively small for commute trips, implying a high level of habituation. In addition, the results from the mixed logit mode choice models clearly show that including a classification of travellers determined by the degree of intrapersonal variation significantly explains mode choice

Maximum interpolable gap length in missing smartphone-based GPS mobility data

Passively-generated location data have the potential to augment mobility and transportation research, as demonstrated by a decade of research. A common trait of these data is a high proportion of missingness. Naïve handling, including list-wise deletion of subjects or days, or linear interpolation across time gaps, has the potential to bias summary results. On the other hand, it is unfeasible to collect mobility data at frequencies high enough to reflect all possible movements. In this paper, we describe the relationship between the temporal and spatial aspects of these data gaps, and illustrate the impact on measures of interest in the field of mobility. We propose a method to deal with missing location data that combines a so-called top-down ratio segmentation method with simple linear interpolation. The linear interpolation imputes missing data. The segmentation method transforms the set of location points to a series of lines, called segments. The method is designed for relatively short gaps, but is evaluated also for longer gaps. We study the effect of our imputation method for the duration of missing data using a completely observed subset of observations from the 2018 Statistics Netherlands travel study. We find that long gaps demonstrate greater downward bias on travel distance, movement events and radius of gyration as compared to shorter but more frequent gaps. When the missingness is unrelated to travel behavior, total sparsity can reach levels of up to 20% with gap lengths of up to 10 min while maintaining a maximum 5% downward bias in the metrics of interest. Temporal aspects can increase these limits; sparsity occurring in the evening or night hours is less biasing due to fewer travel behaviors

Activity-Based Household Travel Survey Through Smartphone Apps in Tennessee

RES 2020-19Activity-based household travel surveys (HTS) are one of primary data sources for many research fields at Tennessee Department of Transportation (TDOT). Traditional HTS methods are often costly, time-consuming, less scalable, and difficult to achieve high quality and accuracy. Recent years have witnessed a fast-growing interest in conducting HTS through smartphone apps to address survey issues and improve quality of collected survey data. A research project on activity based HTS through smartphone apps for both Android and iOS has been performed. The overarching goal of this research project is to develop an effective, economical, scalable HTS solution for TDOT. To achieve this goal, with the guidance and support from TDOT, the research team has 1) developed a smartphone-based effective, scalable, and secure application for household travel surveys that can span from days to months, 2) integrated fine-grained location information in submitted travel data by leveraging smartphone built-in sensor technologies, and 3) validated the developed HTS application by running a pilot HTS with the application. The pilot survey lasted three months. During the survey study, over 800 people downloaded the mobile apps and registered an account. Over 200 participants have been given a reward for completing the survey. Over 1,800 trips were submitted by those rewarded participants. This research project brings the following benefits to TDOT: 1) A tested, comprehensive smartphone app based HTS solution, 2) Important findings about smartphone app based HTS gained from running the pilot survey study, and 3) An anonymized survey dataset for research exploration obtained from the pilot survey study. A number of key findings as well as recommendations are also generated from this research project and they will help TDOT conduct HTS more effectively and generate more research results in the future

Comparative framework for activity-travel diary collection systems

The needs for cheaper and less intrusive ways to collect activity-travel diaries led scientist to pursue new technologies, e.g., positioning technologies like GPS. While a fully, reliable and widely accepted automatic activity-travel diary collection system is yet to be developed, scientists have presented systems that automate parts of an activity-travel diary collection. In the advent of automated systems, it is important to discuss how to analyse the potential of such systems and how to compare different activity-travel diary collection systems. To achieve this objective, this paper introduces a parallel survey design and a comparison framework for collection systems. The framework can be used as a development tool to optimise system design, to report and monitor progress of different system designs, to objectively weigh benefits in decision making, and to automate systematic analysis. In particular, the framework can be used as a comparison tool to reveal the qualitative difference in the data gathered using different collection systems. To achieve this, the framework defines: 1) a number of activity-travel diary measurement entities (trips and triplegs), entity attributes (e.g., trip purpose, origin / destination, etc.), 2) similarity functions between instances of the same entities, and 3) spatial and temporal quality indices to establish a notion of ground truth. The utility of the proposed framework is demonstrated by analysing the results of a trial survey where data is collected via two activity-travel collection systems: a web-based system (PP) and a smartphone-app-based system (MEILI). PP was collected for one day period and MEILI was used for one week period (with one day overlapping). The results show that half of the trips are captured by both systems, while each system roughly captures the same number of trips as the other. The strengths and weaknesses of MEILI are analysed using the framework on the entire week dataset.QC 20160316</p

Understanding the travel behaviour of immigrants and how and why this changes overtime: A case study of Polish immigrants in Bristol and Weston-super-Mare, UK

Immigrant travel behaviour is an understudied field. The small amount of existing research suggests that immigrants culturally assimilate to car-driving to conform to local travel behaviour norms. Examination of this concept with Polish immigrants in Bristol and Weston-super-Mare revealed that cultural conformity is not a driving factor in immigrants post-immigration transition towards car-driving. Instead this occurs as a consequence of the interaction of life-events, life-stage, and structural-context. Using two-stage semi-structured life-course interviews, aided by visual prompts for memory recall and analysis, the travel behaviour journeys of 26-Polish immigrants in Bristol and WSM are examined. Throughout the examination, the methodological value of visual life-history methodologies is explored, concluding that visual analytical methods increase research transparency and aid cross-case comparison