App-driven ridesharing platforms are gaining popularity and are transforming urban movement patterns in cities throughout the world. Because of privacy and business considerations, their owners have released little information about riders’ trip-making characteristics. This lack of data prevents planners and modelers from understanding and quantifying the impact of these new modes on regional travel patterns. In 2016, RideAustin, a not-for-profit company, was established to provide mobility-on-demand services in the Austin region. RideAustin released its dataset of over one million trips to researchers to support transportation planning through a better understanding of urban travel flows. This paper presents findings from an in-depth analysis of this dataset and summarizes key aspects of interest to the transportation research community such as the number of riders, drivers, and trips; total vehicle miles including deadhead miles; and terminal times. The paper also presents findings from two case studies that show the competitiveness of RideAustin versus transit and the utilization of the RideAustin system during the South by Southwest festival. While some of the metrics cannot be readily transferred to other regions, several findings can be used by planners and modelers as they integrate rideshare systems within their planning and modeling frameworks. We also believe that some of the research findings may provide insights into a future system of autonomous and shared vehicles.

Recent technological innovations have changed why, when, where, and how people travel. This, along with other changes in the economy, has resulted in declining transit ridership in many U.S. metropolitan regions, including Los Angeles. It is important that transit agencies become data savvy to better align their services with customer demand in an effort to redesign a bus network that is more relevant and reflective of customer needs. This paper outlines a new data intelligence program within the Los Angeles County Metropolitan Transportation Authority (LA Metro) that will allow for data-driven decision-making in a nimble and flexible fashion. One resource available to LA Metro is their smart farecard data. The analysis of 4 months of data revealed that the top 5% of riders accounted for over 60% of daily trips. By building heuristics to identify transfers, and by tracking riders through space and time to systematically identify home and work locations, transit trip tables by time of day and purpose were extracted. The transit trip tables were juxtaposed against trip tables generated using disaggregate anonymized cell phone data to measure transit market shares and to evaluate transit competitiveness across several measures such as trip length, travel times relative to auto, trip purpose, and time of day. Relying on observed trips as opposed to simulated model results, this paper outlines the potential of using Big Data in transit planning. This research can be replicated by agencies across the U.S. as they reverse declining ridership while competing with data-savvy technology-driven competitors.

This paper describes the findings from the California Vehicle Inventory and Use Survey (CA-VIUS) which was administered between June 2016 and January 2018 and obtained data from a total of 11,118 fleets and 14,790 trucks. The surveys were segmented by registration, geography, vehicle type, and vehicle age, and the data collection effort exceeded sampling targets across almost all segments. The CA-VIUS is the largest statewide commercial vehicle data collection effort in the United States and will replace the 2002 National VIUS in transportation planning and emissions studies throughout California. Currently, the wealth of information provided by the survey is supporting the development of the California Statewide Freight Forecasting Model which is a fine-grained behavioral freight model. This model will allow California Department of Transportation and its partners to make more informed infrastructure and operational investment decisions. The CA-VIUS data will also be useful for researchers and practitioners hoping to understand the impacts and benefits of commercial vehicle movements on air quality, economic activity, safety, and vehicle usage. This paper documents key sampling and survey approaches, but mainly focuses on the key findings observed in the survey. This is a practical paper geared towards practitioners who are seeking to analyze a new VIUS survey and those who wish to implement one of their own.

The objective of this paper is to highlight important differences between taxis and transportation network companies (TNCs) in a large urban area. We analyze the publicly available dataset from Chicago which includes taxi and transportation network company (TNC) utilization and the level of service measures from five months in 2013–2014 and the same five months in 2018–2019. We compare and contrast the data from these two points in time to document utilization of taxis and TNCs and to measure differences in travel times, travel distances, fares, destinations served, and the spatial and temporal distribution of these trips. Travel to and from airports has been evaluated separately owing to the exceptionally high number of trips they generate. Striking differences between pooled and unpooled TNC trip volumes and other travel metrics have been assessed to highlight their operational diversity despite being considered as the same mode. The exploratory analysis has been carried out across the shared-ride, time, and mode dimensions. The study revealed both similarities and differences in taxi trip characteristics between the two evaluation periods and also outlined how the ridehailing market has grown over the years despite the near stagnation in population and employment in the city. We believe that assessing how taxis have fared through this time and highlighting the intrinsic differences between how the old and new mode of on-demand ride services coexist is important. This study aims to help understand how new-age mobility services are impacting transportation in one of the largest cities in the U.S.

Activity-based models (ABMs) that simulate travel are becoming commonplace because of their value in supporting analysis of policy-driven scenarios. Because of the complex nature of ABMs, only a limited number of data sources provide the detail necessary for the estimation and calibration of these models. Of those sources, household travel surveys are becoming increasingly central to the development and calibration of ABMs. These models mimic rational decision making and use a hierarchical decision-making framework that prioritizes mandatory travel and activities, resulting in constrained time for other, nonmandatory activities. Therefore, it is critical that expanded household surveys represent mandatory travel and activity characteristics accurately. Traditionally, household surveys have been expanded to match household-level demographics, such as household size and number of vehicles. More recent weighting frameworks have included person-level demographics, such as worker status and age. However, there is limited research, if any, looking into the role of employment-level attributes (e.g., journey-to-work flow data) within the weighting procedure. This research built on the body of work in the areas of survey expansion and synthetic population generation to incorporate two employment-level variables, industry-level employment totals and home-to-work flow patterns, in the expansion process. Further, the employment-level variables were matched at different spatial resolutions: ( a) industry-level employment totals were matched at the regionwide level, and ( b) home-to-work patterns were matched at the subregional level to improve travel duration distributions. The resulting weights were then contrasted with results from traditional expansion methods by summarizing a variety of variables that are suitable for model validation.