scholarly journals Travel Demand Management – Possibilities of influencing travel behaviour

2013 ◽  
Vol 41 (1) ◽  
pp. 45 ◽  
Author(s):  
Mattias Juhász
2011 ◽  
Vol 38 (4) ◽  
pp. 433-443 ◽  
Author(s):  
Hamid Zaman ◽  
Khandker M. Nurul Habib

Travel demand management (TDM) for achieving sustainability is now considered one of the most important aspects of transportation planning and operation. It is now a well known fact that excessive use of private car results inefficient travel behaviour. So, from the TDM perspective, it is of great importance to analyze travel behaviour for improving our understanding on how to influence people to reduce car use and choose more sustainable modes such as  carpool, public transit, park & ride, walk, bike etc. This study attempts an in-depth analysis of commuting mode choice behaviour using a week-long commuter survey data set collected in the City of Edmonton. Using error correlated nested logit model for panel data, this study investigates sensitivities of various factors including some specific TDM policies such as flexible office hours, compressed work week etc. Results of the investigation provide profound understanding and guidelines for designing effective TDM policies.


2021 ◽  
pp. 1-15
Author(s):  
Anu P. Alex

Activity based travel demand modelling involves lot of uncertainty due to the complex and varying decision making behaviour of each individual. This study contributes to the literature by assessing the suitability of Support Vector Machine (SVM) in modelling the activity pattern and travel behaviour of workers. Activity and travel behaviour of workers consists of decision outcomes, which can be modelled as classification and regression problems. SVM is a good classifier and regressor with good testing and learning capability, hence the present study used SVM for modelling. It was found that support vector machine models are well performing to predict the activity pattern and travel behaviour of workers. The SVM models developed in the study predicts the temporal variation of mode wise work activity generation. Prediction of temporal mode share of commuters is advantageous to policy makers to experiment the implementation of temporary Travel Demand Management (TDM) actions effectively.


2019 ◽  
Vol 51 (3) ◽  
pp. 7-19 ◽  
Author(s):  
Anu P. Alex ◽  
V. S. Manju ◽  
Kuncheria P. Isaac

Travel demand models are required by transportation planners to predict the travel behaviour of people with different socio-economic characteristics. Travel behaviour of students act as an essential component of travel demand modelling. This behaviour is reflected in the educational activity travel pattern, the timing, sequence and mode of travel of students. Roads in the vicinity of schools are adversely affected during the school opening and closing hours. It enhances the traffic congestion, emission and safety problems around schools. It is necessary to improve the safety of school going children by understanding the present travel behaviour and to develop efficient sustainable traffic management measures to reduce congestion in the vicinity of schools. It is possible only if the travel behaviour of educational activities are studied. This travel behaviour is complex in nature and lot of uncertainty exists. Selection of modelling technique is very important for modelling the complex travel behaviour of students. This leads to the importance of application of artificial intelligence (AI) techniques in this area. AI techniques are highly developed in twenty first century due to the advancements in computer, big data and theoretical understanding. It is proved in the literature that these techniques are suitable for modelling the human behaviour. However, it has not been used in behaviourally oriented activity based modelling. This study is aimed to develop a model system to predict the daily travel behaviour of students using artificial intelligence technique, ANN. These ANN models were then compared with the conventional econometric models developed. It was observed that artificial intelligence models provide better results than econometric models in predicting the activity-travel behaviour of students. These models were further applied to study the variation in activity-travel behaviour, if short term travel-demand management measures like promoting walking for educational activities are implemented. Thus the study established that artificial intelligence can replace the conventional econometric methods for modelling the activity-travel behaviour of students. It can also be used for analysing the impact of short term travel demand management measures.


Author(s):  
Kristina M. Currans ◽  
Gabriella Abou-Zeid ◽  
Nicole Iroz-Elardo

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.


Sign in / Sign up

Export Citation Format

Share Document