Showing papers by "Lina Kattan published in 2020"

Analysis of demand–supply gaps in public transit systems based on census and GTFS data: a case study of Calgary, Canada

Abstract: Bridging the gap between demand and supply in transit service is crucial for public transportation management, as planning actions can be implemented to generate supply in high demand areas or to improve upon inefficient deployment of transit service in low transit demand areas. This study aims to introduce feasible approaches for measuring gap types 1 and 2. Gap type 1 measures the gap between public transit capacity and the number of public transit riders per area, while gap type 2 measures the gap between demand and supply as a normalized index. Gap type 1 provides a value that is more realistic than gap type 2, but it requires detailed passenger data that is not always readily available. Gap type 2 is a practical alternative when the detailed passenger data is unavailable because it uses a weighting scheme to estimate demand values. It also uses a newly proposed normalization method called M-score, which allows for a longitudinal gap analysis where yearly gap patterns and trends can be observed and compared. A 5-year gap analysis of Calgary transit is used as a case study. This work presents a new perspective of hourly gaps and proposes a gap measurement approach that contributes to public transit system planning and service improvement.

Compound generalized extreme value distribution for modeling the effects of monthly and seasonal variation on the extreme travel delays for vulnerability analysis of road network

Abstract: This paper proposes a new class of extreme value distribution called compound generalized extreme value (CGEV) distribution for investigating the effects of monthly and seasonal variation on extreme travel delays in road networks. Since the frequency and severity of extreme events are highly correlated to the variation in weather conditions as an extrinsic cause of incidents and long delays, monthly and seasonal changes in weather contributes to extreme travel time variability. The change in driving behavior, which itself varies according to road/weather conditions, also contributes to the monthly and seasonal variation in observed extreme travel times. Therefore, it is critical to model the effect of monthly and seasonal changes on observed extreme travel delays on road networks. Based on the empirically revealed linear relationship between mean and standard deviation (SD) of extreme travel delays for both monthly and seasonal levels, two multiplicative error models are formulated. The CGEV distribution is then obtained by linking the two multiplicative error models and forming a compound distribution that characterizes the overall variation in extreme travel delay. The CGEV distribution parameters are calibrated and the underlying assumptions that are used to derive the CGEV distribution are validated using multi-year observed travel time data from the City of Calgary road network. The results indicate that accounting for the seasonality by identifying seasonal specific parameters provides a flexible and not too complex CGEV distribution that is shown to outperform the traditional GEV distribution. Finally, the application of the proposed CGEV distribution is evaluated in the context of road network vulnerability taking into account the observed probability of extreme event occurrences and the link importance. This derived data-driven vulnerability index incorporates a wealth of information related to both network topography in terms of connectivity and the dynamic interaction between travel demand and supply. This new data-driven vulnerability measure can thus be used as a decision support tool to inform decision-makers in prioritizing improvements to critical links to enhance overall network vulnerability, reliability, and resilience.

Transit Signal Priority Along a Signalized Arterial: A Passenger-based Approach

Abstract: This article develops a passenger-based priority for transit buses by balancing the trade-offs between the benefits at major streets and delays on side streets. A rule-based Transit Signal Priority (TSP) is set to assign priority to scheduled-based transit vehicles based on: their schedule adherence, passenger occupancy, and passengers waiting at downstream stops. The minimum number of bus passengers required to receive priority is obtained using deterministic queueing theory for the two cases of green extension and red truncation. VISSIM simulation software is used to evaluate the performance of the developed TSP approach, comparing it with: existing, unconditional, and no-TSP scenarios. This evaluation assessed performance measures for major streets, crossing streets, and the network level. The simulation demonstrated that a passenger-based TSP results in a significant decrease in travel time and side-street delay, compared to existing priority measures (buses will receive priority once every three minutes) on the corridor.

Collisions on local roads: model development and policy level scenario analysis

Abstract: The problem of collisions on local roads has received little specific attention despite the considerable number of such collisions that occur each year. First part of this study identifies the fact...

Principles of Least Action in Urban Traffic Flow

Abstract: Drawing on the strong connection between traffic modelling and physics we apply the principle of least action to traffic flow in an urban setting.