A congested and dwell time dependent transit corridor assignment model

Abstract

This research proposes an equilibrium assignment model for congested public transport corridors in urban areas. In this model, journey times incorporate the effect of bus queuing on travel times and boarding and alighting passengers on dwell times at stops. The model also considers limited bus capacity leading to longer waiting times and more uncomfortable journeys. The proposed model is applied to an example network, and the results are compared with those obtained in a recent study. This is followed by the analysis and discussion of a real case application in Santiago de Chile. Finally, different boarding and alighting times and different vehicle types are evaluated. In all cases, demand on express services tends to be underestimated by using constant dwell time assignment models, leading to potential planning errors for these lines. The results demonstrate the importance of considering demand dependent dwell times in the assignment process, especially at high demand levels when the capacity constraint should also be considered.

Limited Stop Services Design Considering Variable Dwell Time and Operating Capacity Constraints

“…The lower level is based on an equilibrium assignment model for public transport corridors. The journey times are influenced by the time the buses spend at the stops due to passengers boarding and alighting and the effects of congestion in terms of discomfort and longer waiting times at stops, proposed by Alonso et al [21], which takes its basis from the approach of De Cea and Fernández [26] and Larraín and Muñoz [23].…”

“…! is the perceived journey time, which includes bus waiting time queuing to service a stop as well as a "discomfort" term [21,27,28] depending on vehicle occupancy:…”

“…The omission of these items leads to an inexact estimation of both the user costs (perceived total travel time) and the operating costs. The dwell time variable and its importance, as well as the effect of congestion not only on the passengers but also on the vehicle journey times has already been highlighted as a necessary contribution to the state of the art [10,15,16,18,20] and their importance in the estimation of passenger assignment and required fleet size has already been reported by Alonso et al [21] and Lam et al [22] respectively. In this sense the proposed model in this article fills these gaps highlighted in the literature.…”

“…Later, Yu et al [24] included a penalty function to model dwell times depending on the on-board congestion, however, these times did not depend on the real demand at each stop and the authors did assume an average value to be penalized. Only the research carried out by Alonso et al [21] incorporates demand dependent dwell times in the transit assignment problem, also including capacity constraints both in the vehicles and at the stops, due to bus bunching effects. This approach of variable dwell times is similar than the one followed later by Tang et al [25] in an optimization model to find fares and operational strategies in a transit network without considering congestion effects nor transfers.…”

“…The proposed model contributes to the state of the art forward by designing a single solution containing all the main operating variables that affect public transport corridors such as: frequency, vehicle capacity, required fleet size and the stops to service on each line. This will complement existing research and add the following contributions: (i) the first, and in our view, the most important contribution, is based on the effect of boarding and alighting times depending on demand, not only on user journey times, but also on the turnaround (cycle) times of the lines which affect the route choice process and the required fleet, respectively; (ii) the proposed design also incorporates the effect of congestion and capacity constraints on the passenger assignment model, modelling the capacity constraint with an increasing function of waiting time [26], the discomfort perceived by the user due to the high vehicle occupancy [27] and the congestion found at the stops themselves due to vehicle saturation [21], all of which are incorporated into the perceived journey times and the cycle times; (iii) the proposal of optimization in a single model addressing stop sequencing along the lines, frequencies, fleet size and vehicle typology and (iv) a demonstration of the method's applicability to a real corridor with real demand (many-to-many).…”