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Prof. Mohan M. Venigalla
Civil and Infrastructure Engineering, George Mason University, Fairfax, VA 220130, USA

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0 Air Quality
0 Big Data Analytics
0 Transportation Planning
0 Sustainable Transportation
0 Shared Mobility

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Journal article
Published: 11 July 2020 in Transportation Research Interdisciplinary Perspectives
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The effect of a planned special event on travel time performance measures varies spatially and temporally based on the type of the planned special event. Evaluating and assessing the effect helps practitioners proactively plan and improve traffic operations within its vicinity during the future planned special event. The objective of this paper, therefore, is to adopt a systemic evaluation method and examine the effect of a planned special event on travel time performance measures, over time and space, from the location of the planned special event. Raw travel time data obtained from a private data source was used to assess the spatial and temporal effects of two planned special events on travel time performance measures. Data were captured for each major road link within 3-miles of each planned special event location. Statistical analyses such as the t-test and Cohen's D were conducted to assess the difference in travel time performance measures during the day of the planned special event compared to the normal day. A statistically significant increase in travel times and travel time variations was observed on the day of the planned special event when compared to the normal day. However, the effect of the increase in travel times was observed to be small, whereas, the effect of the increase in the travel time variation was observed to be higher. Buffer time index (BTI) and planning time index (PTI) were observed to be significantly higher during the NASCAR race and for longer periods compared to the NFL game.

ACS Style

Srinivas S. Pulugurtha; Venkata R. Duddu; Mohan Venigalla. Evaluating spatial and temporal effects of planned special events on travel time performance measures. Transportation Research Interdisciplinary Perspectives 2020, 6, 100168 .

AMA Style

Srinivas S. Pulugurtha, Venkata R. Duddu, Mohan Venigalla. Evaluating spatial and temporal effects of planned special events on travel time performance measures. Transportation Research Interdisciplinary Perspectives. 2020; 6 ():100168.

Chicago/Turabian Style

Srinivas S. Pulugurtha; Venkata R. Duddu; Mohan Venigalla. 2020. "Evaluating spatial and temporal effects of planned special events on travel time performance measures." Transportation Research Interdisciplinary Perspectives 6, no. : 100168.

Original paper
Published: 03 February 2020 in Journal of Big Data Analytics in Transportation
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A sustainable and robust stream of revenues is an essential element of the economic sustainability of bikeshare systems. To this effect, there is an unrelenting need to quantify and understand the impacts of pricing policies and operational considerations on a bikeshare system’s revenue and ridership. A notable gap exists in literature on studies related to the impact of changes in pricing policy on ridership and revenue. The primary objective of this research was to assess the impact of the introduction of a $2 per trip single-trip fare (STF) product for casual users by Capital Bikeshare (CaBi), the bikeshare system in the Washington DC metro area, on its ridership and revenue. Two-year ridership and revenue transaction datasets of CaBi were used in the analysis. A substantial data curation effort was undertaken to fuse elements between the two large transactional datasets. The effort not only facilitated the impact assessment but also enhanced the value of ridership dataset by identifying trips made by casual users by the type of fare product they purchased. The casual bikeshare user revenues were traced to individual bikeshare stations where trips originated, which allowed the comparison between revenues and ridership ‘before’ and ‘after’ the launch of STF at the station level. Over 22 million records on individual bikeshare trips and revenue transactions for 3 years and 330 bikeshare stations were analyzed. The results showed a statistically significant increase in casual user ridership after the introduction of STF. There was a statistically significant decrease in revenue per ride. Statistical tests indicated that these changes might be attributable to the introduction of STF. The methods used in this study are transferable. They can be used for curating ridership data and studying the impacts of bikeshare pricing policy changes on system usage and revenues at various public bikesharing systems with similar characteristics as Capital Bikeshare.

ACS Style

Mohan Venigalla; Shruthi Kaviti; Thomas Brennan. Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips. Journal of Big Data Analytics in Transportation 2020, 2, 1 -16.

AMA Style

Mohan Venigalla, Shruthi Kaviti, Thomas Brennan. Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips. Journal of Big Data Analytics in Transportation. 2020; 2 (1):1-16.

Chicago/Turabian Style

Mohan Venigalla; Shruthi Kaviti; Thomas Brennan. 2020. "Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips." Journal of Big Data Analytics in Transportation 2, no. 1: 1-16.

Journal article
Published: 06 June 2019 in Transportation Research Interdisciplinary Perspectives
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Pricing is one of the major factors that affects ridership and revenue of the bikeshare systems. This paper examines bikeshare users' sensitivity to changes in price and preferences on service by conducting an intercept survey at Capital Bikeshare (CaBi) stations. Monadic price testing approach was used to design survey questions and elicit responses on sensitivity of users to price changes in fare products. Ordered logit regression results indicated that higher household income groups and ‘White’ users are less sensitive to price compared to other income groups and other races/ethnicities, respectively. An illustrative application of the demand curves is presented, which showed that low-income groups are more sensitive to price than the middle and high-income groups. White users were found to be approximately 20% less price sensitive than other races for both casual users purchasing single-trips and annual members. The price elasticities revealed that females are about 30% and 10% more price sensitive than males for single-trip fare (STF) and annual membership, respectively. Also, sightseeing trips are 30% less price sensitive than work trips for STF purchasers. An illustrative application of income-based elasticities indicated that reducing the STF to $1.50 from the current $2.00 per trip and annual membership to $73.00 (from $85 per year) were found to improve both ridership and revenue. It is expected that the contributions from this study would provide insights and guidance on evaluating future pricing policy changes at various bikeshare systems.

ACS Style

Shruthi Kaviti; Mohan M. Venigalla. Assessing service and price sensitivities, and pivot elasticities of public bikeshare system users through monadic design and ordered logit regression. Transportation Research Interdisciplinary Perspectives 2019, 1, 100015 .

AMA Style

Shruthi Kaviti, Mohan M. Venigalla. Assessing service and price sensitivities, and pivot elasticities of public bikeshare system users through monadic design and ordered logit regression. Transportation Research Interdisciplinary Perspectives. 2019; 1 ():100015.

Chicago/Turabian Style

Shruthi Kaviti; Mohan M. Venigalla. 2019. "Assessing service and price sensitivities, and pivot elasticities of public bikeshare system users through monadic design and ordered logit regression." Transportation Research Interdisciplinary Perspectives 1, no. : 100015.

Journal article
Published: 04 March 2019 in Travel Behaviour and Society
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Even though casual users of bikeshare account for a large share of ridership and revenue at public bikeshare systems in North America, very little is known about the characteristics and preferences of casual users and how they compare to registered members. The primary objectives of the study include identifying the similarities and differences between members and casual users along demographics, usage and indicated preferences; and examining and modeling pricing preferences of bikeshare users. An intercept survey was conducted to obtain demographic information, bikeshare usage and various preferences of Capital Bikeshare users in the metro Washington DC area. The survey data was validated against the data from an existing member survey with large sample using goodness of fit tests. Survey participants reported that single trip fare (STF) and annual membership paid at once as their preferred pricing options and a combination of STF, 24-hour pass, and annual membership with monthly installments as their favorable pricing model. Logistic regression findings indicate that, when compared to casual users, registered members are more likely to be White, earn more and reside in the D.C. area. Casual users make fewer bikeshare trips and are less sensitive to the service (station density) compared to members. Gender, age and income distribution do not appear to influence casual fare product choice. Results from this study are useful in policy-making, planning and operations for bikeshare systems.

ACS Style

Shruthi Kaviti; Mohan M. Venigalla; Kimberly Lucas. Travel behavior and price preferences of bikesharing members and casual users: A Capital Bikeshare perspective. Travel Behaviour and Society 2019, 15, 133 -145.

AMA Style

Shruthi Kaviti, Mohan M. Venigalla, Kimberly Lucas. Travel behavior and price preferences of bikesharing members and casual users: A Capital Bikeshare perspective. Travel Behaviour and Society. 2019; 15 ():133-145.

Chicago/Turabian Style

Shruthi Kaviti; Mohan M. Venigalla; Kimberly Lucas. 2019. "Travel behavior and price preferences of bikesharing members and casual users: A Capital Bikeshare perspective." Travel Behaviour and Society 15, no. : 133-145.

Articles
Published: 01 January 2018 in Urban, Planning and Transport Research
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Regional travel-demand forecasting models are complex and cumbersome to use. Furthermore, they are also not sensitive to neighborhood level sustainable land use policies such as transit-oriented developments (TOD). There is a need for developing simple sketch planning tools and methodologies for taking measurements on the impacts of land use policies on mobility and environment. The primary objective of this research is to develop a methodology for deriving household-level emission footprints of auto travel from household travel survey data. The methodology was demonstrated by comparing and contrasting emission footprints for TOD and Non-TOD land uses in the Washington DC metropolitan area. A TOD is defined as the area encircling stations along line-haul Metrorail service. Statistical analyses indicated that Non-TOD emission footprints are significantly higher than the TOD footprints. Differences amongst pairs of TODs showed no statistical significance. Some exceptions to this generalized observation were also noted. The utility of the methodology was also demonstrated by comparing aggregate emission footprints at county level. The methodology can also be used for deriving emission footprints for any logical aggregate group of traffic analysis zones (TAZ). Thus, this research advances the utility of travel surveys to establishing baselines on emission footprints for select geographies.

ACS Style

Mohan Venigalla; Shweta Dixit; Srinivas Pulugurtha. A methodology to derive land use specific auto-trip emission footprints from household travel survey data. Urban, Planning and Transport Research 2018, 6, 111 -128.

AMA Style

Mohan Venigalla, Shweta Dixit, Srinivas Pulugurtha. A methodology to derive land use specific auto-trip emission footprints from household travel survey data. Urban, Planning and Transport Research. 2018; 6 (1):111-128.

Chicago/Turabian Style

Mohan Venigalla; Shweta Dixit; Srinivas Pulugurtha. 2018. "A methodology to derive land use specific auto-trip emission footprints from household travel survey data." Urban, Planning and Transport Research 6, no. 1: 111-128.