The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Vincent Grasset - One of the best experts on this subject based on the ideXlab platform.
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Modelling users behaviour of a carsharing program application of a joint hazard and zero inflated dynamic ordered probability Model
Transportation Research Part A-policy and Practice, 2012Co-Authors: Khandker Nurul Habib, Mohammed Tazul Islam, Catherine Morency, Vincent GrassetAbstract:This paper presents an Econometric Model for the behaviour of carsharing users. The Econometric Model is developed to jointly forecast membership duration, the decision to become an active member in a particular month, and the frequency of monthly usage of active members. The Model is estimated using the membership directory and monthly transaction data of a carsharing program, ‘Communauto Inc.’, based in Montreal, Canada. The Model shows a high degree of fit to the observed dataset and provides many behavioural details of carsharing users. The results are instructive to carsharing planners in devising efficient policies.
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Modelling users' behaviour of a carsharing program: Application of a joint hazard and zero inflated dynamic ordered probability Model
Transportation Research Part A: Policy and Practice, 2012Co-Authors: Khandker M. Nurul Habib, Mohammed Tazul Islam, Catherine Morency, Vincent GrassetAbstract:This paper presents an Econometric Model for the behaviour of carsharing users. The Econometric Model is developed to jointly forecast membership duration, the decision to become an active member in a particular month, and the frequency of monthly usage of active members. The Model is estimated using the membership directory and monthly transaction data of a carsharing program, '. Communauto Inc.', based in Montréal, Canada. The Model shows a high degree of fit to the observed dataset and provides many behavioural details of carsharing users. The results are instructive to carsharing planners in devising efficient policies. © 2011 Elsevier Ltd.
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Understanding members' carsharing (activity) persistency by using Econometric Model
Journal of Advanced Transportation, 2012Co-Authors: Catherine Morency, Vincent Grasset, Khandker M. Nurul Habib, Md Tazul IslamAbstract:Carsharing is an innovative travel alternative that has recently experienced considerable growth and become part of sustainable transportation initiatives. Although carsharing is becoming increasingly a popular alternative transportation mode in North America, it is still an under-researched area. Current research is aimed at better understanding of the behavior of carsharing users. For every member, a two-stage approach microsimulates the probability of being active in any month using a binary probit Model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility-based Model. The Model is estimated using empirical data from one of the largest carsharing companies in North America. The Model estimates reveal that the activity persistency of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. It also shows that some attributes of the traveler (gender, age, and language spoken at home) impact his or her behaviors.
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understanding members carsharing activity persistency by using Econometric Model
Journal of Advanced Transportation, 2012Co-Authors: Catherine Morency, Vincent Grasset, Khandker Nurul Habib, Tazul IslamAbstract:This article reports on a research study undertaken to better understand the behavior of carsharing users, particularly their persistence in using this transportation modality. The authors Model the probability of the users being active (using the carsharing system) as well as their monthly frequency of use, using real transaction datasets (40 months of operation of a Montreal, Canada carsharing company). For every member, a two-stage approach microsimulates the probability of being active in any month using a binary probit Model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility-based Model. Results show that the activity persistence of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. The study also showed that some attributes of the traveler (gender, age, and language spoken at home) have an impact on travel behaviors. The authors conclude that paying more attention to the day-to-day behavior of clients will improve understanding of the carsharing system and contribute to a better assessment of the true market and impacts of carsharing in an urban area.
Khandker Nurul Habib - One of the best experts on this subject based on the ideXlab platform.
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an investigation on mode choice and travel distance demand of older people in the national capital region ncr of canada application of a utility theoretic joint Econometric Model
Transportation, 2015Co-Authors: Khandker Nurul HabibAbstract:This paper uses a utility-theoretic joint Econometric Model to investigate the factors affecting mode choice and travel distance of older people (age 65+) along with the interrelationship between these two. The main objective is to investigate the effects of modal accessibility on travel distance requirements of older people and resulting implications of social exclusions. Empirical Models are estimated by using a household travel survey conducted in the National Capital Region (NCR) of Canada. The empirical Model reveals that older people living in the NCR often need to travel longer distances for engaging in various activities because of poor modal accessibility. It is also evident that the effects of accessibility are not the same across the region. Older people living far from the central business district (CBD) need to travel longer distances compared to the older people living close to the CBD. With an increasingly older population in the region, an increasing of risk of transportation-related social exclusion may be implied because of the need for longer distance travel to engage in activities. Most importantly, such implications in the NCR is driven more by the region’s urban form and land use patterns than the performance of the regional transportation system.
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Modelling users behaviour of a carsharing program application of a joint hazard and zero inflated dynamic ordered probability Model
Transportation Research Part A-policy and Practice, 2012Co-Authors: Khandker Nurul Habib, Mohammed Tazul Islam, Catherine Morency, Vincent GrassetAbstract:This paper presents an Econometric Model for the behaviour of carsharing users. The Econometric Model is developed to jointly forecast membership duration, the decision to become an active member in a particular month, and the frequency of monthly usage of active members. The Model is estimated using the membership directory and monthly transaction data of a carsharing program, ‘Communauto Inc.’, based in Montreal, Canada. The Model shows a high degree of fit to the observed dataset and provides many behavioural details of carsharing users. The results are instructive to carsharing planners in devising efficient policies.
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understanding members carsharing activity persistency by using Econometric Model
Journal of Advanced Transportation, 2012Co-Authors: Catherine Morency, Vincent Grasset, Khandker Nurul Habib, Tazul IslamAbstract:This article reports on a research study undertaken to better understand the behavior of carsharing users, particularly their persistence in using this transportation modality. The authors Model the probability of the users being active (using the carsharing system) as well as their monthly frequency of use, using real transaction datasets (40 months of operation of a Montreal, Canada carsharing company). For every member, a two-stage approach microsimulates the probability of being active in any month using a binary probit Model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility-based Model. Results show that the activity persistence of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. The study also showed that some attributes of the traveler (gender, age, and language spoken at home) have an impact on travel behaviors. The authors conclude that paying more attention to the day-to-day behavior of clients will improve understanding of the carsharing system and contribute to a better assessment of the true market and impacts of carsharing in an urban area.
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investigating the role of social networks in start time and duration of activities trivariate simultaneous Econometric Model
Transportation Research Record, 2011Co-Authors: Khandker Nurul Habib, Juan Antonio CarrascoAbstract:In the context of improving understanding and Modeling the capabilities of activity-scheduling processes in travel behavior, this paper explores the role of social networks in the start time and the duration of social activities. The study was performed with a trivariate joint Econometric Model that was capable of capturing the correlation between unobserved influential factors causing the endogeneity of these three key decisions. The Model captures the relevance not only of sociodemographic variables but also of the social network dimension for travelers, or the with whom variable, that is, individuals with whom travelers perform social activities. A particularly relevant case is the role of travel time to social activities, which has a positive effect on longer durations and late start times and which acts as a link between these two basic dimensions (start time and duration) of activity scheduling. The results confirm the relevance of the social context in an episode's temporal characteristics and illu...
Linglin Ni - One of the best experts on this subject based on the ideXlab platform.
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a spatial Econometric Model for travel flow analysis and real world applications with massive mobile phone data
Transportation Research Part C-emerging Technologies, 2018Co-Authors: Linglin Ni, Xiaokun Wang, Xiqun ChenAbstract:Abstract Cellular signaling data provide a massive and emerging source for acquiring urban origin–destination (OD) travel flow information, supporting decision making on large-scale mobility enhancement, and enabling the exploration of factors that influence travel demand. This study investigates the effects of population, facilities, and transit accessibility on travel flows between traffic analysis zones. A spatial Econometric Model is employed for the OD travel flow analysis by integrating massive mobile data with other explanatory features of urban regions. The results of real-world applications in Hangzhou, China, show that: (I) all coefficients of the origin dependence, destination dependence, and OD dependence are statistically significant, which verifies the consideration of the spatial interdependence in the OD flow Modeling; (II) all of the permanent population, number of facilities, and transit accessibility have positive correlations with travel flows; and (III) travel time, as expected, is negatively correlated with the travel flow volume. Finally, policy implications are discussed, further contributing to the design of urban land use and transportation policies.
Catherine Morency - One of the best experts on this subject based on the ideXlab platform.
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Modelling users behaviour of a carsharing program application of a joint hazard and zero inflated dynamic ordered probability Model
Transportation Research Part A-policy and Practice, 2012Co-Authors: Khandker Nurul Habib, Mohammed Tazul Islam, Catherine Morency, Vincent GrassetAbstract:This paper presents an Econometric Model for the behaviour of carsharing users. The Econometric Model is developed to jointly forecast membership duration, the decision to become an active member in a particular month, and the frequency of monthly usage of active members. The Model is estimated using the membership directory and monthly transaction data of a carsharing program, ‘Communauto Inc.’, based in Montreal, Canada. The Model shows a high degree of fit to the observed dataset and provides many behavioural details of carsharing users. The results are instructive to carsharing planners in devising efficient policies.
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Modelling users' behaviour of a carsharing program: Application of a joint hazard and zero inflated dynamic ordered probability Model
Transportation Research Part A: Policy and Practice, 2012Co-Authors: Khandker M. Nurul Habib, Mohammed Tazul Islam, Catherine Morency, Vincent GrassetAbstract:This paper presents an Econometric Model for the behaviour of carsharing users. The Econometric Model is developed to jointly forecast membership duration, the decision to become an active member in a particular month, and the frequency of monthly usage of active members. The Model is estimated using the membership directory and monthly transaction data of a carsharing program, '. Communauto Inc.', based in Montréal, Canada. The Model shows a high degree of fit to the observed dataset and provides many behavioural details of carsharing users. The results are instructive to carsharing planners in devising efficient policies. © 2011 Elsevier Ltd.
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Understanding members' carsharing (activity) persistency by using Econometric Model
Journal of Advanced Transportation, 2012Co-Authors: Catherine Morency, Vincent Grasset, Khandker M. Nurul Habib, Md Tazul IslamAbstract:Carsharing is an innovative travel alternative that has recently experienced considerable growth and become part of sustainable transportation initiatives. Although carsharing is becoming increasingly a popular alternative transportation mode in North America, it is still an under-researched area. Current research is aimed at better understanding of the behavior of carsharing users. For every member, a two-stage approach microsimulates the probability of being active in any month using a binary probit Model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility-based Model. The Model is estimated using empirical data from one of the largest carsharing companies in North America. The Model estimates reveal that the activity persistency of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. It also shows that some attributes of the traveler (gender, age, and language spoken at home) impact his or her behaviors.
-
understanding members carsharing activity persistency by using Econometric Model
Journal of Advanced Transportation, 2012Co-Authors: Catherine Morency, Vincent Grasset, Khandker Nurul Habib, Tazul IslamAbstract:This article reports on a research study undertaken to better understand the behavior of carsharing users, particularly their persistence in using this transportation modality. The authors Model the probability of the users being active (using the carsharing system) as well as their monthly frequency of use, using real transaction datasets (40 months of operation of a Montreal, Canada carsharing company). For every member, a two-stage approach microsimulates the probability of being active in any month using a binary probit Model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility-based Model. Results show that the activity persistence of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. The study also showed that some attributes of the traveler (gender, age, and language spoken at home) have an impact on travel behaviors. The authors conclude that paying more attention to the day-to-day behavior of clients will improve understanding of the carsharing system and contribute to a better assessment of the true market and impacts of carsharing in an urban area.
Xiqun Chen - One of the best experts on this subject based on the ideXlab platform.
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a spatial Econometric Model for travel flow analysis and real world applications with massive mobile phone data
Transportation Research Part C-emerging Technologies, 2018Co-Authors: Linglin Ni, Xiaokun Wang, Xiqun ChenAbstract:Abstract Cellular signaling data provide a massive and emerging source for acquiring urban origin–destination (OD) travel flow information, supporting decision making on large-scale mobility enhancement, and enabling the exploration of factors that influence travel demand. This study investigates the effects of population, facilities, and transit accessibility on travel flows between traffic analysis zones. A spatial Econometric Model is employed for the OD travel flow analysis by integrating massive mobile data with other explanatory features of urban regions. The results of real-world applications in Hangzhou, China, show that: (I) all coefficients of the origin dependence, destination dependence, and OD dependence are statistically significant, which verifies the consideration of the spatial interdependence in the OD flow Modeling; (II) all of the permanent population, number of facilities, and transit accessibility have positive correlations with travel flows; and (III) travel time, as expected, is negatively correlated with the travel flow volume. Finally, policy implications are discussed, further contributing to the design of urban land use and transportation policies.
