64 research outputs found
Model Accuracy Data for Post-Construction Evaluation of Forecast Accuracy in Minnesota
No full textThis data was used for the project: Post-Construction Evaluation of Forecast Accuracy
Parthasarathi, Pavithra; Levinson, David (Minnesota Department of Transportation, 2009)This research evaluates the accuracy of demand forecasts using a sample of recently-completed projects in
Minnesota and identifies the factors influencing the inaccuracy in forecasts. The forecast traffic data for this study
is drawn from Environmental Impact Statements (EIS), Transportation Analysis Reports (TAR) and other forecast
reports produced by the Minnesota Department of Transportation (Mn/DOT) with a horizon forecast year of 2010
or earlier. The actual traffic data is compiled from the database of traffic counts maintained by the Office of
Transportation Data and Analysis at Mn/DOT. Based on recent research on forecast accuracy, the inaccuracy of
traffic forecasts is estimated as a ratio of the forecast traffic to the actual traffic. The estimation of forecast
inaccuracy also involves a comparison of the socioeconomic and demographic assumptions, the assumed networks
to the actual in-place networks and other travel behavior assumptions that went into generating the traffic forecasts
against actual conditions. The analysis indicates a general trend of underestimation in roadway traffic forecasts
with factors such as highway type, functional classification, and direction playing an influencing role. Roadways
with higher volumes and higher functional classifications such as freeways are subject to underestimation
compared to lower volume roadways/functional classifications. The comparison of demographic forecasts shows a
trend of overestimation while the comparison of travel behavior characteristics indicates a lack of incorporation of
fundamental shifts and societal changes.Minnesota Department of TransportationParthasarthi, Pavithra K; Levinson, David M. (2017). Model Accuracy Data for Post-Construction Evaluation of Forecast Accuracy in Minnesota. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/D6RW2Z
Network structure and metropolitan mobility
No full textJTLU vol. 7, no. 2, pp. 153-170 (2014)This research aims to develop quantitative measures that capture various aspects of the underlying network structure, using aggregate level travel data from fifty metropolitan areas across the US. The influence of these measures on system performance is then tested using statistical regression models. The results corroborate that the quantitative measures of network structure affect the system performance. The results from this analysis can be used to develop network design guidelines that can be used to address current transportation problems.Parthasarathi, Pavithra. (2014). Network structure and metropolitan mobility. Retrieved from the University Digital Conservancy, 10.5198/jtlu.v7i2.494
Freeway Service Patrols: A Stated Preference Analysis of Insurance Values
No full textIn this chapter, a Stated Preference (SP) analysis was carried out to identify the factors that influence people to choose highway assistance services (FSP) over private assistance services (PAS). The Los-Angeles FSP was used as a test case and the B/C ratios were also calculated based on the utilitytheFSPprovidestoanindividual. Differentvalueswerechosenforthe average time of waiting of the FSP and the B/C ratios were calculated in each case. The results indicate that the probability of an individual choosing the highway assistance services depends on the attributes of the program like the time of waiting for assistance and cost of waiting for assistance. The B/C ratios for the Los Angeles FSP were in the range 6.2–6.3.California PATH Program, California Department of TransportationLevinson, David M; Parthasarathi, Pavithra; Gillen, David W. (2004). Freeway Service Patrols: A Stated Preference Analysis of Insurance Values. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/179876
Using Twin Cities Destinations and Their Accessibility as a Multimodal Planning Tool Report
No full textThis study uses accessibility as a performance measure to evaluate a matrix of future land use and network scenarios for planning purposes. Previous research has established the coevolution of transportation and land use, demonstrated the dependence of accessibility on both, and made the case for the use of accessibility measures as a planning tool. This study builds off of these findings by demonstrating the use of accessibility-based performance measures on the Twin Cities metropolitan area. This choice of performance measure also allows for transit and highway networks to be compared side-by-side. A zone-to-zone travel time matrix was computed using Stochastic User Equilibrium (SUE) assignment with travel time feedback to trip distribution. A database of schedules was used on the transit networks to assign transit routes. This travel time data was joined with the land use data from each scenario to obtain the employment, population, and labor accessibility from each traffic analysis zone (TAZ) within specified time ranges. Tables of person-weighed accessibility were computed for 20 minutes with zone population as the weight for employment accessibility and zone employment as the weight for population and labor accessibility. The person-weighted accessibility results were then used to evaluate the planning scenarios. The results show that centralized population and employment produce the highest accessibility across all networks.Minnesota Department of Transportation, Research Services SectionAnderson, Paul; Levinson, David; Parthasarathi, Pavithra. (2012). Using Twin Cities Destinations and Their Accessibility as a Multimodal Planning Tool Report. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/121730
Network Structure and Travel Time Perception
No full textRoad networks have an underlying structure. This structure is defined by the layout, arrangement and the connectivity of the individual network elements, the road segments and their intersections. The differences in network structure exist across and within networks. Travelers perceive and respond to these differences in underlying network structure and complexity. This paper extends the analysis to understanding the underlying theory of why network structure influences travel. Specifically the focus is on the influence of network structure on travel time perception. The hypothesis here is that network design influences traveler perceptions, more specifically the perceptions of travel distance and time. This perception of travel distance and time in turn influences the actual travel by affecting choice of destination, mode, route, and whether to engage in activities.Parthasarathi, Pavithra. (2013). Network Structure and Travel Time Perception. Retrieved from the University Digital Conservancy, http://dx.doi.org/doi:10.1371/journal.pone.0077718
Accessibility Futures
No full textThis study uses accessibility as a performance measure to evaluate a matrix of future land use and network scenarios for planning purposes. Previous research has established the coevolution of transportation and land use, demonstrated the dependence of accessibility on both, and made the case for the use of accessibility measures as a planning tool. This study builds off of these findings by demonstrating the use of accessibility-based performance measures on the Twin Cities Metropolitan Area. This choice of performance measure also allows for transit and highway networks to be compared side-by-side. A zone to zone travel time matrix was computed using SUE assignment with travel time feedback to trip distribution. A database of schedules was used on the transit networks to assign transit routes. This travel time data was joined with the land use data from each scenario to obtain the employment, population, and labor accessibility from each TAZ within specified time ranges. Tables of person- weighed accessibility were computed for 20 minutes with zone population as the weight for employment accessibility and zone employment as the weight for population and labor accessibility. The person-weighted accessibility results were then used to evaluate the planning scenarios. The results show that centralized population and employment produce the highest accessibility across all networks.Minnesota Department of TransportationAnderson, Paul; Levinson, David M; Parthasarathi, Pavithra. (2013). Accessibility Futures. Retrieved from the University Digital Conservancy, http://dx.doi.org/10.1111/tgis.12024
Network structure and the journey to work: An intra-metropolitan analysis
No full textThis paper aims to look at the variation of network structure within a metropolitan area and relate it to observed travel, measured here as the average travel time to work. The Minor Civil Divisions (MCD) within the Twin Cities (Minneapolis, St. Paul) metropolitan area are chosen for this analysis. Quantitative measures, compiled from various sources, are used to capture the various aspects of network structure within each MCD. The variation of these measures within the metropolitan area is analyzed using spatial analyses. The measures of network structure are then related to observed travel using statistical regression models. The results confirm a relation between network structure and travel and point to the importance of understanding the underlying street network structure.RP Braun/CTS Chair in Transportation,
Florida Department of TransportationParthasarathi, Pavithra; Levinson, David M. (2012). Network structure and the journey to work: An intra-metropolitan analysis. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/179817
Post-Construction Evaluation of Forecast Accuracy
No full textThis research evaluates the accuracy of demand forecasts using a sample of recently-completed projects in
Minnesota and identifies the factors influencing the inaccuracy in forecasts. The forecast traffic data for this study
is drawn from Environmental Impact Statements (EIS), Transportation Analysis Reports (TAR) and other forecast
reports produced by the Minnesota Department of Transportation (Mn/DOT) with a horizon forecast year of 2010
or earlier. The actual traffic data is compiled from the database of traffic counts maintained by the Office of
Transportation Data and Analysis at Mn/DOT. Based on recent research on forecast accuracy, the inaccuracy of
traffic forecasts is estimated as a ratio of the forecast traffic to the actual traffic. The estimation of forecast
inaccuracy also involves a comparison of the socioeconomic and demographic assumptions, the assumed networks
to the actual in-place networks and other travel behavior assumptions that went into generating the traffic forecasts
against actual conditions. The analysis indicates a general trend of underestimation in roadway traffic forecasts
with factors such as highway type, functional classification, and direction playing an influencing role. Roadways
with higher volumes and higher functional classifications such as freeways are subject to underestimation
compared to lower volume roadways/functional classifications. The comparison of demographic forecasts shows a
trend of overestimation while the comparison of travel behavior characteristics indicates a lack of incorporation of
fundamental shifts and societal changes.Minnesota Department of TransportationParthasarathi, Pavithra; Levinson, David. (2009). Post-Construction Evaluation of Forecast Accuracy. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/151312
Post-Construction Evaluation of Traffic Forecast Accuracy
No full textThis research evaluates the accuracy of demand forecasts using a sample of recently-completed projects in Minnesota and identifies the factors influencing the inaccuracy in forecasts. The forecast traffic data for this study is drawn from Environmental Impact Statements (EIS), Transportation Analysis Reports (TAR) and other forecast reports produced by the Minnesota Department of Transportation (Mn/DOT) with a horizon forecast year of 2010 or earlier. The actual traffic data is compiled from the database of traffic counts maintained by the Office of Traffic Forecasting and Analysis section at Mn/DOT. Based on recent research on forecast accuracy, the (in)accuracy of traffic forecasts is estimated as a ratio of the forecast traffic to the actual traffic. The estimation of forecast (in)accuracy also involves a comparison of the socioeconomic and demographic assumptions, the assumed networks to the actual in-place networks and other travel behavior assumptions that went into generating the traffic forecasts against actual conditions. The analysis indicates a general trend of underestimation in roadway traffic forecasts with factors such as highway type, functional classifications, direction playing an influencing role. Roadways with higher volumes and higher functional classifications such as freeways are subject to underestimation compared to lower volume roadways/functional classifications. The comparison of demographic forecasts shows a trend of overestimation while the comparison of travel behavior characteristics indicates a lack of incorporation of fundamental shifts and societal changes.Minnesota Department of TransportationParthasarathi, Pavithra; Levinson, David M. (2010). Post-Construction Evaluation of Traffic Forecast Accuracy. Retrieved from the University Digital Conservancy, http://dx.doi.org/doi:10.1016/j.tranpol.2010.04.010
Accessibility Futures.
No full textThis study uses accessibility as a performance measure to evaluate a matrix of future land use and network scenarios for planning purposes. Previous research has established the coevolution of transportation and land use, demonstrated the dependence of accessibility on both, and made the case for the use of accessibility measures as a planning tool. This study builds off of these findings by demonstrating the use of accessibility-based performance measures on the Twin Cities Metropolitan Area. This choice of performance measure also allows for transit and highway networks to be compared side-by-side. A zone to zone travel time matrix was computed using SUE assignment with travel time feedback to trip distribution. A database of schedules was used on the transit networks to assign transit routes. This travel time data was joined with the land use data from each scenario to obtain the employment, population, and labor accessibility from each TAZ within specified time ranges. Tables of person- weighed accessibility were computed for 20 minutes with zone population as the weight for employment accessibility and zone employment as the weight for population and labor accessibility. The person-weighted accessibility results were then used to evaluate the planning scenarios. The results show that centralized population and employment produce the highest accessibility across all networks.Accessibility, Forecasting, Travel Demand, Scenarios, Trends, Transportation, Land Use.
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