Anahid NABAVI - Academia.edu (original) (raw)

Papers by Anahid NABAVI

Transportation Research Procedia, 2015

This paper is an output of a French national project called iSpace&Time aiming to provide a 4 dim... more This paper is an output of a French national project called iSpace&Time aiming to provide a 4 dimensional platform of an urban dynamics. In order to express the urban traffic, we took an advantage of the mobile phone data to investigate the behavior of the origin destination flow within the Paris and its suburb aiming to explore the different mode of the transportation. Indeed the spatiotemporal heterogeneities of mobile phone data make the task of mode of transportation separation very challenging, sometimes even impossible. Thus, by exploring the OD matrix in order to revealing any probable continues trends or any dominant trace of the flow stating a specific mode of transportation, the commuter trains happened to be somehow detectable. Then an individual-based step-by-step approach is proposed to estimate mode of transportation from mobile phone data. Analyzing the individual trajectory, the decision is given to a segment level with respect to different measures. An early promising outcome consists of detection of the segments in which people would take the metro.

Transportation Research Record: Journal of the Transportation Research Board, 2012

Analysis of public transport system performance and level of service in urban areas is essential.... more Analysis of public transport system performance and level of service in urban areas is essential. Dynamic modelling of traffic conditions, passenger demand and transit operations are important to adequately represent the complexity and interactions among these components in modern public transportation systems. This paper presents a transit simulation model designed to support evaluation of operations, planning and control, especially in the context of Advanced Public Transportation Systems (APTS). Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems taking into account the main sources of service uncertainty and stochasticity. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a real-world high-demand bus line in the Tel Aviv metropolitan area under various scenarios and shows that important phenomena such as bus bunching are reproduced realistically. Simulated running times and headway distributions are compared with field data and shows the model's capability to replicate observed data.

Transportation Research Record: Journal of the Transportation Research Board, 2011

... Dwell times at stops are determined as a function of passenger activity at the stop, crowding... more ... Dwell times at stops are determined as a function of passenger activity at the stop, crowding on board, and physical stop characteristics (eg, bay Cats, Larijani, Koutsopoulos, and Burghout 53 or in lane, bus stop capacity) (21). ... 35 30 25 Number of Through P asseng e rs and P ...

Transportation Research Record Journal of the Transportation Research Board

Transit operations involve several inherent sources of uncertainty, including dispatch time from ... more Transit operations involve several inherent sources of uncertainty, including dispatch time from the origin terminal, travel time between stops, and dwell time at stops. Bus-holding control strategies are a prominent method applied by transit operators to improve transit performance and level of service. The common practice is to regulate departures from a limited number of stops by holding buses until their scheduled departure time. An analysis of the performance of a high-frequency bus line in Stockholm, Sweden, based on automatic vehicle location data showed that this control strategy was not effective in improving service regularity along the line. The analysis also indicated that drivers adjusted their speed according to performance objectives. Implications of a control strategy that regulates departures from all stops on the basis of the headways of the preceding bus and the following bus were evaluated with BusMezzo, a transit operations simulation model. The results suggest ...

Transportation Research Procedia, 2015

This paper is an output of a French national project called iSpace&Time aiming to provide a 4 dim... more This paper is an output of a French national project called iSpace&Time aiming to provide a 4 dimensional platform of an urban dynamics. In order to express the urban traffic, we took an advantage of the mobile phone data to investigate the behavior of the origin destination flow within the Paris and its suburb aiming to explore the different mode of the transportation. Indeed the spatiotemporal heterogeneities of mobile phone data make the task of mode of transportation separation very challenging, sometimes even impossible. Thus, by exploring the OD matrix in order to revealing any probable continues trends or any dominant trace of the flow stating a specific mode of transportation, the commuter trains happened to be somehow detectable. Then an individual-based step-by-step approach is proposed to estimate mode of transportation from mobile phone data. Analyzing the individual trajectory, the decision is given to a segment level with respect to different measures. An early promising outcome consists of detection of the segments in which people would take the metro.

Transportation Research Record: Journal of the Transportation Research Board, 2012

Analysis of public transport system performance and level of service in urban areas is essential.... more Analysis of public transport system performance and level of service in urban areas is essential. Dynamic modelling of traffic conditions, passenger demand and transit operations are important to adequately represent the complexity and interactions among these components in modern public transportation systems. This paper presents a transit simulation model designed to support evaluation of operations, planning and control, especially in the context of Advanced Public Transportation Systems (APTS). Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems taking into account the main sources of service uncertainty and stochasticity. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a real-world high-demand bus line in the Tel Aviv metropolitan area under various scenarios and shows that important phenomena such as bus bunching are reproduced realistically. Simulated running times and headway distributions are compared with field data and shows the model's capability to replicate observed data.

Transportation Research Record: Journal of the Transportation Research Board, 2011

... Dwell times at stops are determined as a function of passenger activity at the stop, crowding... more ... Dwell times at stops are determined as a function of passenger activity at the stop, crowding on board, and physical stop characteristics (eg, bay Cats, Larijani, Koutsopoulos, and Burghout 53 or in lane, bus stop capacity) (21). ... 35 30 25 Number of Through P asseng e rs and P ...

Transportation Research Record Journal of the Transportation Research Board

Transit operations involve several inherent sources of uncertainty, including dispatch time from ... more Transit operations involve several inherent sources of uncertainty, including dispatch time from the origin terminal, travel time between stops, and dwell time at stops. Bus-holding control strategies are a prominent method applied by transit operators to improve transit performance and level of service. The common practice is to regulate departures from a limited number of stops by holding buses until their scheduled departure time. An analysis of the performance of a high-frequency bus line in Stockholm, Sweden, based on automatic vehicle location data showed that this control strategy was not effective in improving service regularity along the line. The analysis also indicated that drivers adjusted their speed according to performance objectives. Implications of a control strategy that regulates departures from all stops on the basis of the headways of the preceding bus and the following bus were evaluated with BusMezzo, a transit operations simulation model. The results suggest ...