Felipe Delgado

Real-Time Control of Buses in a Transit Corridor Based on Vehicle Holding and Boarding Limits

A real-time mathematical programming model of buses operating on a transit corridor that incorporates vehicle-capacity constraints is proposed. The objective for the model is to minimize the total times experienced by all passengers in the system, from the moment they arrive at a stop to the moment they reach their destination. Two control policies are considered: (a) vehicle holding, which is applicable at any stop, and (b) boarding limits that constrain the number of passengers entering a vehicle even when the vehicle is at less than physical capacity, to increase operating speed. The objective function is quadratic, but not convex with linear constraints. This problem is solved by using MINOS in a reasonable amount of computation time. A case study in a high-demand scenario shows that the proposed control achieves reductions in the objective function of more than 22% and 12% compared with no control and only holding strategies, respectively.

Bus Control Strategy Application: Case Study of Santiago Transit System☆

Buses have an inherent tendency to bunch due to randomness in passenger demand and congestion. Many sophisticated control strategies have been developed to reduce bus bunching, however, few of them have been implemented in high frequency real services. Building upon a control strategy comprised of a rolling horizon mathematical programming model that yields the optimal holding time that minimizes user-waiting times, the authors have developed real-time software and implemented it on two bus services in Santiago using different technologies to communicate the instructions to bus drivers. The results presented in this paper are encouraging, on the days the system was implemented less bus bunching was observed, which translated in fewer headway-irregularity fines. Moreover lower passenger fare evasion was observed when the bus control strategy was used.

Integrated Real-Time Transit Signal Priority Control for High-Frequency Segregated Transit Services

Bus bunching affects transit operations by increasing passenger waiting time and variability. To tackle this phenomenon, a wide range of control strategies has been proposed. However, none of them have considered station and interstation control together. In this study station and interstation control were tackled to determine the optimal vehicle control strategy for various stops and traffic lights in a single service transit corridor. The strategy minimized the total time that users must devote to making a trip, taking into account delays for transit and general traffic users. Based on a high-frequency, capacity-constrained, and unscheduled service (no timetable) for which real-time information about bus position (GPS) and bus load (automated passenger counter) is available, this study focused on strategies for traffic signal priority in the form of green extension considered together with holding buses at stops and limiting passenger boarding at stops. The decisions on transit signal priority were made according to a rolling horizon scheme in which effects over the whole corridor were considered in every single decision. The proposed strategy was evaluated in a simulated environment under different operational conditions. Results showed that the proposed control strategy achieves reductions in the excess delay for transit users close to 61.4% compared with no control, while general traffic increases only by 1.5%.

A risk averse approach to the capacity allocation problem in the airline cargo industry

In air cargo transportation, capacity can be reserved via allotment, which are long-term contracts with fixed price, and free, which is the space not assigned to allotment contracts. In this later case, reservations are made closer to the departure date, and normally higher tariffs are charged. The demand, the tariff, and the show-up rate for the free mode are stochastic. We consider risk neutral and risk averse formulations, using the Conditional Value-at-Risk as a risk measure. We solve the resulting problems using the Sample Average Approximation and test our models with nine experiments representing different demand patterns using real data from a major airline.