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.

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 What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

2021 ◽  
Vol 13 (16) ◽  
pp. 9324
Author(s):  
Sujae Kim ◽  
Sangho Choo ◽  
Sungtaek Choi ◽  
Hyangsook Lee
Keyword(s):  
Traffic Congestion ◽  
Latent Class ◽  
Travel Demand ◽  
Public Transit ◽  
Latent Class Model ◽  
Influencing Factor ◽  
Demand Management ◽  
Related Factors ◽  
Travel Demand Management ◽  
Trip Frequency

Mobility as a Service (MaaS), which integrates public and shared transportation into a single service, is drawing attention as a travel demand management strategy aimed at reducing automobile dependency and encouraging public transit. In particular, there have been few studies that recognize traffic congestion during peak hours and identify related factors for practical application. The purpose of this study is to explore what factors affect Seoul commuters’ mode choice including MaaS. A web-based survey that 161 commuters participated in was conducted to collect information about personal, household, and travel attributes, together with their mode preference for MaaS. A latent class model was developed to classify unobserved latent groups based on trip frequency by means and to identify factors influencing mode-specific utilities (in particular, MaaS service) for each class. The result shows that latent classes are divided into two groups (public transit-oriented commuters and balanced mode commuters). Most variables have significant impacts on choice for MaaS. The coefficient of MaaS choice of Class 1 and Class 2 were different. These findings suggest there is a difference between the classes according to trip frequency by means as an influencing factor in MaaS choice.

scholarly journals TRAVEL DEMAND MANAGEMENT: SHORT REVIEW OF THE SPECIAL ISSUE

Transport ◽  
2014 ◽  
Vol 29 (3) ◽  
pp. 233-234 ◽  
Author(s):  
Ying-En Ge ◽  
Chunyan Tang ◽  
Olegas Prentkovskis ◽  
Wafaa Saleh ◽  
Raimundas Junevičius ◽  
...  
Keyword(s):  
Travel Demand ◽  
Demand Management ◽  
Short Review ◽  
Special Issue ◽  
Travel Demand Management

"Travel demand management: short review of the special issue" Transport, 29(3), pp. 233-234

scholarly journals Solving Traffic Congestion through Street Renaissance: A Perspective from Dense Asian Cities

Urban Science ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 18 ◽  
Author(s):  
Liang Wen ◽  
Jeff Kenworthy ◽  
Xiumei Guo ◽  
Dora Marinova
Keyword(s):  
Land Use Planning ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Supply And Demand ◽  
Demand Management ◽  
Holistic Approach ◽  
Residential Areas ◽  
Travel Demand Management ◽  
Traffic Demand ◽  
Work Related

Traffic congestion is one of the most vexing city problems and involves numerous factors which cannot be addressed without a holistic approach. Congestion cannot be narrowly tackled at the cost of a city’s quality of life. Focusing on transport and land use planning, this paper examines transport policies and practices on both the supply and demand sides and finds that indirect travel demand management might be the most desirable solution to this chronic traffic ailment. The concept of absorption of traffic demand through the renaissance of streets as a way for traffic relief is introduced from two perspectives, with some examples from dense Asian urban contexts to demonstrate this. Firstly, jobs–housing balance suggests the return of production activities to residential areas and sufficient provision of diverse space/housing options to deal with work-related traffic. The second approach is to promote the street as a multi-activity destination rather than a thoroughfare to access dispersed daily needs, and to advocate more street life to diminish non-commuting traffic. Based on this, suggestions for better transport planning policies are put forward.

scholarly journals Multimode Traffic Travel Behavior Characteristics Analysis and Congestion Governance Research

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Wen Li ◽  
Wei Feng ◽  
Hua-zhi Yuan
Keyword(s):  
Sustainable Development ◽  
Public Transportation ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Demand Management ◽  
Congestion Management ◽  
Traffic Demand ◽  
Modern Cities ◽  
Behavior Characteristics

The rapid aggregation of modern urban population and the rapid growth of car travel lead to traffic congestion, environmental pollution, and other problems. In view of the limited land resources in our country, it is impractical to meet residents’ travel demand by blindly increasing traffic supply. Therefore, addressing the urban road congestion problem for sustainable development of modern cities, the paper makes research on residents’ travel behavior characteristics and travel preference under the condition of multimodal transportation to formulate reasonable traffic demand management strategy for the guide on public traffic demand, bus priority strategy, and congestion management. The operation characteristic of each transportation mode is analyzed by comparing its related traffic and economic characteristics. Multimode traffic choice behavior is discussed by establishing multiple logistic regression models to analyze the main influencing factors to travelers’ social and economic attributes, travel characteristics, and preference based on travel survey data of urban residents. The paper proposes the development of an urban public transportation system and travelling mode shift from cars to public transportation as reasonable travel structure for congestion management and sustainable development of modern cities.

Modeling the Effectiveness of Infrastructure and Travel Demand Management Measures to Improve Traffic Congestion during Typhoons

2018 ◽  
Vol 2672 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Wisinee Wisetjindawat ◽  
Sybil Derrible ◽  
Amirhassan Kermanshah
Keyword(s):  
Failure Probability ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Climate Model ◽  
Demand Management ◽  
Travel Time Reliability ◽  
Travel Demand Management ◽  
Management Measures ◽  
Departure Time ◽  
Tokai Region

Many commuters find themselves stranded during natural disasters like typhoons. In the Tokai region in Japan, many road sections become heavily congested during typhoons, with some commuters reporting homebound trips taking more than four times longer than usual because of road flooding at several locations. Although large typhoons are considered extreme events (in terms of magnitude), they occur frequently (i.e., several times per year), substantiating the need for better preparedness. Nonetheless, it is impossible to predict exactly which roads are going to be flooded during a typhoon. As a result, in this study, a stochastic modeling approach was used that assigns a failure probability to each road segment based on climate model outputs for the region. Using this stochastic model, the travel time reliability between any given origin–destination pair can be determined. By applying this model to the road network of the Tokai region, two major measures were identified that could be implemented to reduce severe congestion during a typhoon. First, targeted infrastructure management measures can be implemented to strengthen heavily used roads, thus reducing the failure probability of major roads. Second, travel demand management measures can be implemented, such as asking commuters to leave their workplace or school one or two hours after their normal departure time. Overall, it was found that strengthening heavily used roads has a bigger impact in relieving congestion than delaying departure time, but that combining both strategies achieves the best results.

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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