Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level

2020 ◽  
pp. 036119812096479
Author(s):  
Kristina M. Currans ◽  
Gabriella Abou-Zeid ◽  
Nicole Iroz-Elardo
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Management Strategies ◽  
Land Development ◽  
Future Research ◽  
Alternative Mode ◽  
Travel Demand Management ◽  
Development Level ◽  
Automobile Demand

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

scholarly journals SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE

2015 ◽  
Vol 27 (6) ◽  
pp. 529-538 ◽  
Author(s):  
Ying-En Ge ◽  
Olegas Prentkovskis ◽  
Chunyan Tang ◽  
Wafaa Saleh ◽  
Michael G. H. Bell ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
State Of The Art ◽  
Demand Management ◽  
Relevant Literature ◽  
Short Review ◽  
Active Management ◽  
Demand Side ◽  
Travel Demand Management

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks.

Gender-Based Analysis of Work Trip Mode Choice of Commuters in Suburban Montreal, Canada, with Stated Preference Data

2005 ◽  
Vol 1924 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Zachary Patterson ◽  
Gordon Ewing ◽  
Murtaza Haider
Keyword(s):  
Travel Demand ◽  
Stated Preference ◽  
Mode Choice ◽  
Demand Management ◽  
Management Strategies ◽  
Travel Demand Management ◽  
Men And Women ◽  
Work Trip ◽  
Gender Based ◽  
Commuting Patterns

Transportation literature suggests that men and women differ in their commuting patterns and in their propensity to switch between travel options. In North America, it is expected that women will have an increasing impact on travel demand. As such, differences in female responses to travel demand management strategies are likely to become increasingly important as governments try to curtail travel demand. This paper uses a 1994 stated preference survey of suburban commuters in Montreal, Canada, to determine whether there is evidence for differences between men and women in the factors that affect work trip choices, to quantify those differences, and to suggest what the differences imply for travel demand management in the future in Montreal. The main conclusions of this paper are as follows. First, women and men should be modeled separately for work trip mode choice. Second, three main differences appear from the econometric models: women are less likely to choose public transit than men; women are more likely to choose to rideshare; and women are less time-sensitive in regard to commuting than men are.

scholarly journals The Interaction between E-Shopping and Shopping Trips: An Analysis with 2017 NHTS

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chenlei Xue ◽  
Qunqi Wu ◽  
Maopeng Sun ◽  
Pengxia Bai ◽  
Yang Chen
Keyword(s):  
Standard Deviation ◽  
Information And Communication Technologies ◽  
Travel Behavior ◽  
Structural Equation ◽  
Travel Demand ◽  
Management Strategies ◽  
Shopping Behavior ◽  
Equation Modeling ◽  
Travel Demand Management ◽  
Shopping Trips

Advances in information and communication technologies (ICT) have dramatically changed the nature of shopping and the way people travel. As this technology becomes deeply rooted in people’s lives, understanding the interplay between this way and personal travel is becoming increasingly important for planners. Using travel diary data from the 2017 National Household Travel Survey (NHTS) data for structural equation modeling (SEM) analysis, it revealed the interaction between e-shopping and shopping trips and the factors that affect this bidirectional relationship. Results show that e-shopping motivates shopping trips, and in-store shopping inhibits online shopping. It can be obtained that the increase of one standard deviation of e-shopping will increase the shopping trip by 0.17 standard deviation. When shopping trips increase by one standard deviation, e-shopping behavior also decreases by 0.12 standard deviation. The results also demonstrated that e-shopping and shopping travel behavior is heterogeneous across a variety of exogenous factors such as personal attributes, household characteristics, geography, travel distance/duration, and travel mode. Identifying the interaction may help formulate better transportation policies and lay the foundation for travel demand management strategies to reduce the stress on the transportation system and meet individual travel needs.

scholarly journals Park And Ride Facilities within Kuala Lumpur Conurbation: How willing are private vehicle commuters to use?

2019 ◽  
Vol 4 (12) ◽  
pp. 313
Author(s):  
Anis Adibah Ibrahim ◽  
Norlida Abdul Hamid ◽  
Azimah Daud
Keyword(s):  
Travel Demand ◽  
Demand Management ◽  
Publishing House ◽  
Management Strategies ◽  
Kuala Lumpur ◽  
Vehicle Ownership ◽  
Travel Demand Management ◽  
Private Vehicle ◽  
Park And Ride ◽  
The City

The rapid transformation of Kuala Lumpur conurbation has seen a rise in the demand for private vehicle ownership among commuters, thus leading to an increase in the overall level of congestion within the city center. Towards reducing such issues and focusing on the significance of travel demand management, this study examined the willingness of private vehicle users in using the park and ride facilities. A convenience survey was conducted on 199 respondents through multiple linear regression. It was found that socioeconomic, trip, and technology characteristics have a significant relationship with a willingness to park and ride. The result may assist future transport planners in improving the overall parking management strategies of the city centre.Keywords: Willingness, park and ride, Kuala Lumpur conurbation, regressioneISSN: 2398-4287 © 2019. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v4i12.1943

Household Activity Rescheduling in Response to Automobile Reduction Scenarios

2002 ◽  
Vol 1807 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Sean T. Doherty ◽  
Martin Lee-Gosselin ◽  
Kyle Burns ◽  
Jean Andrey
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Small Sample ◽  
Lifestyle Changes ◽  
Travel Demand Management ◽  
Fuel Prices ◽  
Response Strategies ◽  
Household Members

Forecasting the enduring and wider implications of emerging travel demand management and automobile reduction policies has proved to be a challenging task. Travel behavior researchers point to the need for more in-depth research into the underlying activity-travel scheduling processes as a means to improve the ability to do so. The objective of this research is to explore the household rescheduling and adaptation process to vehicle reduction scenarios. Descriptive results from two, small-sample, in-depth experiments are presented. The first experiment focused on households’ response to a fuel prices increase, whereas the second focused on the response of two-vehicle households to long-term removal of one vehicle from the household. Results indicate that households are aware of a broad range of possible adaptation strategies, including not only mode changes but also a wide variety of changes in activities, planning, and longer-term lifestyle changes. When people were asked to actually implement such stated strategies under realistic conditions, a much more elaborate behavioral response was elicited. This included multiple rescheduling decisions involving several activities and household members over the course of a day or even several days. Thus, even relatively straightforward stated response strategies often lead to interconnected primary and secondary effects on observed activities and travel, realized through a sequence of rescheduling decisions over time and space and across household members. These results suggest that an explicit accounting of rescheduling decision sequences in forecasting models would enhance their behavioral validity and accuracy.

scholarly journals MODELING A SPATIO-TEMPORAL INDIVIDUAL TRAVEL BEHAVIOR USING GEOTAGGED SOCIAL NETWORK DATA: A CASE STUDY OF GREATER CINCINNATI

2017 ◽  
Vol IV-4/W2 ◽  
pp. 207-214
Author(s):  
M. Saeedimoghaddam ◽  
C. Kim
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Predictive Accuracy ◽  
Demand Management ◽  
Large Data ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Travel Demand Management ◽  
Travel Destination ◽  
Spatio Temporal

Understanding individual travel behavior is vital in travel demand management as well as in urban and transportation planning. New data sources including mobile phone data and location-based social media (LBSM) data allow us to understand mobility behavior on an unprecedented level of details. Recent studies of trip purpose prediction tend to use machine learning (ML) methods, since they generally produce high levels of predictive accuracy. Few studies used LSBM as a large data source to extend its potential in predicting individual travel destination using ML techniques. In the presented research, we created a spatio-temporal probabilistic model based on an ensemble ML framework named “Random Forests” utilizing the travel extracted from geotagged Tweets in 419 census tracts of Greater Cincinnati area for predicting the tract ID of an individual’s travel destination at any time using the information of its origin. We evaluated the model accuracy using the travels extracted from the Tweets themselves as well as the travels from household travel survey. The Tweets and survey based travels that start from same tract in the south western parts of the study area is more likely to select same destination compare to the other parts. Also, both Tweets and survey based travels were affected by the attraction points in the downtown of Cincinnati and the tracts in the north eastern part of the area. Finally, both evaluations show that the model predictions are acceptable, but it cannot predict destination using inputs from other data sources as precise as the Tweets based data.

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