The Experts below are selected from a list of 88815 Experts worldwide ranked by ideXlab platform
H. Scott Matthews - One of the best experts on this subject based on the ideXlab platform.
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Are travelers substituting between Transportation Network companies (TNC) and public buses? A case study in Pittsburgh
Transportation, 2020Co-Authors: Rick Grahn, H. Scott Matthews, Sean Qian, Chris HendricksonAbstract:Transportation Network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of Transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve Transportation Network efficiency.
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Socioeconomic and usage characteristics of Transportation Network company (TNC) riders
Transportation, 2019Co-Authors: Rick Grahn, Corey D. Harper, Chris Hendrickson, Zhen Qian, H. Scott MatthewsAbstract:The widespread adoption of smartphones followed by an emergence of Transportation Network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.
Rick Grahn - One of the best experts on this subject based on the ideXlab platform.
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Are travelers substituting between Transportation Network companies (TNC) and public buses? A case study in Pittsburgh
Transportation, 2020Co-Authors: Rick Grahn, H. Scott Matthews, Sean Qian, Chris HendricksonAbstract:Transportation Network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of Transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve Transportation Network efficiency.
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Socioeconomic and usage characteristics of Transportation Network company (TNC) riders
Transportation, 2019Co-Authors: Rick Grahn, Corey D. Harper, Chris Hendrickson, Zhen Qian, H. Scott MatthewsAbstract:The widespread adoption of smartphones followed by an emergence of Transportation Network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.
Chris Hendrickson - One of the best experts on this subject based on the ideXlab platform.
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Are travelers substituting between Transportation Network companies (TNC) and public buses? A case study in Pittsburgh
Transportation, 2020Co-Authors: Rick Grahn, H. Scott Matthews, Sean Qian, Chris HendricksonAbstract:Transportation Network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of Transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve Transportation Network efficiency.
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Socioeconomic and usage characteristics of Transportation Network company (TNC) riders
Transportation, 2019Co-Authors: Rick Grahn, Corey D. Harper, Chris Hendrickson, Zhen Qian, H. Scott MatthewsAbstract:The widespread adoption of smartphones followed by an emergence of Transportation Network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.
Sze Chun Wong - One of the best experts on this subject based on the ideXlab platform.
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Transportation Network Reliability
Transportmetrica B: Transport Dynamics, 2013Co-Authors: William H. K. Lam, Sze Chun WongAbstract:This special issue on Transportation Network Reliability is based on papers selected from the 5th International Symposium on Transportation Network Reliability(INSTR) held in Hong Kong on 18–19 Dec...
Don Mackenzie - One of the best experts on this subject based on the ideXlab platform.
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Transportation Network company wait times in greater seattle and relationship to socioeconomic indicators
Journal of Transport Geography, 2016Co-Authors: Ryan Hughes, Don MackenzieAbstract:Abstract Transportation Network companies (TNC) which use smartphone apps to connect travelers to drivers for point to point, intra-city trips have expanded rapidly in recent years, yet the impacts of their services on urban Transportation systems, for good or ill, are not fully understood. These services may increase access to Transportation for some individuals, but it is unclear whether these benefits are equitably distributed across different neighborhoods. In this paper, we explore the spatial variability in wait times for a TNC vehicle throughout the Seattle, WA region, testing whether areas with lower average income or a greater percentage of minorities experience different waiting times than other areas. We collected approximately 1 million observations of estimated waiting times, quasi-randomly sampled over approximately two months in 2015. We analyzed spatial and temporal patterns using local regression, which suggested lower wait times in densely populated areas of the Seattle region, and the lowest wait times during midday hours. We developed a spatial error regression model to investigate relationships between wait times and socioeconomic indicators at the census block group (CBG) level. We find that conditional on other covariates, expected waiting times are longer in CBGs with higher average income, and shorter in CBGs with greater population density and employment density. After adjusting for differences in density and income, a higher percentage of minorities in a CBG is associated with longer waiting times late at night and shorter waiting times during the day, with an average effect of close to zero. Geographically weighted regression indicates that the strength, and in some cases the sign, of these relationships varies throughout the Seattle region. Overall, the results suggests that Transportation Network companies offer higher performance in dense urban areas, and that adequate access to TNC services is not necessarily restricted to areas that are “white and wealthy.”
