Elnaz Irannezhad

Modeling Transit Passenger Choices of Access Stop

A logit discrete choice model is developed to investigate the choice of transit access stop (i.e., departure stop). The model incorporates different components of the transit service between given origin–destination pairs at given times. A choice set generation algorithm is developed to create the set of access stop choices and calculate the time-dependent impedances from each departure stop to the destination. The correlation between the choices is treated at two levels: (a) the mode of travel and (b) the route of travel. A nested logit model structure is adopted to account for the dependencies among the choices on the same mode, and a correction term is proposed that captures the correlation between the stop choices that results from the commonality of the routes that serve the destination. The data are from the household travel survey of 2009 in Southeast Queensland, Australia and include travel records on three public transit modes: bus, train, and ferry. The case study analysis of Southeast Queensland shows the effectiveness of the proposed correction by demonstrating improvements in modeling the choice of access stop. The research concludes a new finding that the choice of access stop is affected not only by the attributes of the transit path between the journey ends but also, significantly and directly, by the attributes of the departure stop itself.

Copula-Based Joint Discrete–Continuous Model of Road Vehicle Type and Shipment Size

A major issue in freight modeling is the interrelationship between logistics choices that can be seen as a learning process that shippers or carriers undertake to optimize their logistics process, with the aim of minimizing their cost, maximizing their level of service, or both. This study looked at the interrelated decisions of vehicle type choice and shipment size in urban freight transportation by formulating a model that considered (a) the nature of these two dependent variables via a joint discrete–continuous model, (b) the correlation between the two decisions via a copula-based approach, (c) the differences in decision making between carriers and shippers via the estimation of two models, and (d) the relaxation of the assumption of pure utility maximization via a hybrid utility–regret specification. Results show differences between shippers’ and carriers’ preferences. These differences were logical because many urban shippers owned an efficient fleet of commercial vehicles, whereas carriers evaluated alternatives to maximize their aggregated utility and minimize their direct costs. Results also show the importance of considering jointly the two decisions as well as the relevance of using a hybrid utility–regret formulation for the cost. Practical findings emerge from the model: (a) when faced with night delivery and intercity trips, carriers were more likely to use heavier vehicles and more voluminous shipments, whereas smaller shipments were preferred during the afternoon peak hour; (b) urban shippers tended to deliver larger shipments during night by light trucks but preferred trailers for longer distances; and (c) commodity types played a role in these joint decisions because some commodities were more likely to be transported by for-hire carriers and others were more likely to be transported by shippers.

Modeling the efficiency of a port community system as an agent-based process

Abstract: We present an agent-based method which makes use of reinforcement learning in order to estimate the efficiency of a Port Community System. We have evaluated the method using two weeks of observations of import containers at the Port of Brisbane as a case study. Three scenarios are examined. The first scenario evaluates the observed container delivery by individual shipping lines and estimates the consignments allocated to the various road carriers based on optimizing the individual shippers total logistics cost. The second scenario implies that, in the optimum case, all agents (shipping lines and road carriers) communicate and cooperate through a single portal. The objective of cooperation is in sharing vehicles and creating tours to deliver shipments to several importers in order to reduce total logistics costs, while physical and time window constraints are also considered. The third scenario allows for some agents to occasionally decide to act based on individual costs instead of total combined logistics costs. The results of this study indicate an increase in the efficiency of the whole logistics process through cooperation, and the study provides a prototype of a Port Community System to support logistics decisions.

Effects of Passage Restrictions for Heavy Vehicles on Tour Trips in Urban Networks

one of the differences of freight modeling to passenger modeling is its complex pattern in supply chain process. Probability of tour is a function of network and impedances among destinations and fleet constraints. It is obvious that in network with passage restrictions (in residential streets and collectors) for heavy vehicles, the number of destinations in each tour (tour length) will increase and instead it may increase the cost of the users. This paper discusses about activities of major agents, in freight movement, in our case study which is the second major city in Iran. Cost of shippers and number of destinations in tour, will be compared in two states. The first state is no restriction and the second state is passage restriction in some residential streets.