Saeid Saidi

Bio: Saeid Saidi is an academic researcher from University of Calgary. The author has contributed to research in topics: Headway & Urban rail transit. The author has an hindex of 7, co-authored 15 publications receiving 134 citations. Previous affiliations of Saeid Saidi include Massachusetts Institute of Technology.

Long-term planning for ring-radial urban rail transit networks

Abstract: Extensive work exists on regular rail network planning. However, few studies exist on the planning and design of ring-radial rail transit systems. With more ring transit lines being planned and built in Asia, Europe and the America's, a detailed study on ring transit lines is timely. An analytical model to find the optimal number of radial lines in a city for any demand distribution is first introduced. Secondly, passenger route choice for different rail networks is analyzed, for a many-to-many Origin-Destination (OD) demand distribution, based on a total travel time cost per passenger basis. The routes considered are: (1) radial lines only; (2) ring line only or radial lines and ring line combined; or (3) direct access to a destination without using the rail system. Mathematica and Matlab are used to code the route choice model. A cost-benefit optimization model to identify the feasibility and optimality of a ring line is proposed. Unlike simulations and agent-based models, this model is shown to be easily transferable to many ring-radial transit networks. The City of Calgary is used as an example to illustrate the applicability of each model. The existing urban rail network and trip distribution are major influencing factors in judging the feasibility and optimal location of the ring line. This study shows the potential net benefit of introducing a ring line by assessing changes in passengers’ costs. The changes in passenger cost parameters, such as ride cost and access cost, are shown to greatly influence the feasibility of a ring line.

A generalized framework for complex urban rail transit network analysis

Abstract: The paper introduces a new framework for analysis of complex urban rail transit networks based on generalized passenger costs. We have developed a robust framework for the long-term planning and modeling of rail transit networks that are idealized in a radio-centric form. The model is used to compare the performance of different rail transit networks in term of generalized passenger costs. The conventional comparative studies of networks are either survey data driven or based on graph theory which cannot consider the impact key parameters such as demand distribution and different passenger costs. In this paper, a network analysis of urban rail transit systems with topologies similar to six cities (London, Moscow, Tokyo, Paris, Berlin, and Madrid) is conducted. The examined transit systems are assessed using identical model inputs such as length of network and trip distribution patterns. This model is easily applicable to most radio and/or centric transit networks.

Microsimulation Evaluation of the Potential Impacts of Vehicle-to-Vehicle Communication (V2V) in Disseminating Warning Information under High Incident Occurrence Conditions

Abstract: This paper focuses on assessing the benefits of vehicle-to-vehicle (V2V) communication in high incident cases resulting from extreme conditions, such as adverse weather conditions. V2V capabilities are simulated and tested through the use of two Paramics Applications Programming Interfaces (APIs). One API randomly creates incidents, and the API simulates the dissemination of V2V communication between individual V2V vehicles. The developed APIs are rigorously evaluated on an urban arterial network by comparing various scenarios with and without V2V. The results of all experiments demonstrate the overall effectiveness of V2V in improving safety and improving network travel time for high and moderate congestion level scenarios.

Stochastic bus schedule coordination considering demand assignment and rerouting of passengers

Abstract: Schedule coordination is a proven strategy to improve the connectivity and service quality for bus networks, whereas current research mostly optimizes schedule design using the a priori knowledge of users’ routings and ignores the behavioural reactions to coordination status. This study proposes a novel stochastic bus schedule coordination design with demand assignment and passenger rerouting in case of transfer failure. To this end, we develop a bi-level programming model in which the schedule design (headways and slack times) and passenger route choice are determined simultaneously via two travel strategies: non-adaptive and adaptive routings. In the second strategy, transfer passengers would modify their paths in case of missed connection. In this way, the expected network flow distribution is dependent on both the transfer reliability and network structure. The upper-level problem is formulated as a mixed integer non-linear program with the objective of minimizing the total system cost, including both operation cost and user cost, while the lower-level problem is route choice (pre-trip and on-trip) model for timed-transfer service. A more generalized inter-ratio headways scenario is also taken into account. A heuristic algorithm and the method of successive averages are comprehensively applied for solving the bi-level model. Results show that when the rerouting behaviour is considered, more cost-effective schedule coordination scheme with less slack times can be achieved, and ignoring such effect would underestimate the efficacy of schedule coordination scheme.

Community resilience-driven restoration model for interdependent infrastructure networks

Abstract: Critical interdependent infrastructure networks such as water distribution, natural gas pipeline, electricity power, communication and transportation systems provide the essential necessities for societies and their utilization is the backbone of everyday processes such as production, health, convenience and many more. Often cascading dysfunctionality or disruption in these critical infrastructure networks triggers chain reactions of blackouts or blockages through the system of highly interconnected infrastructure networks, and the disruption of surrounding societies. For the planning of restoration processes and resilience of these, social aspects and demographics should also be considered to assign and mitigate the possible social risks associated with these disruptions. In this work, we study the restoration planning of critical interdependent infrastructure networks after a possible disruptive event by mainly emphasizing on the vulnerability indices of interacting society. We integrate (i) a resilience-driven multi-objective mixed-integer programming formulation to schedule the restoration process of disrupted network components in each network with (ii) an index of social vulnerability that is geographically distributed. We present an illustrative example of the proposed integrated model that focuses on studying the community resilience in Shelby County, TN, United States.