Evacuation plan evaluation: Assessment of vehicular evacuation schemes by means of an analytical dynamic traffic model

An evacuation model is posed, combining a dynamic travel demand component, an adaptive travel choice behaviour component, and a dynamic network loading component. The travel demand component considers the primary choice facing the endangered residents, whether to participate in the evacuation, and if so, when to depart. The travel choice behaviour component considers the secondary choice facing the evacuees, where to seek refuge and by which route to travel towards this safe destination. The network loading component considers both the dynamic traffic and hazard conditions, and propagates the evacuees through the infrastructure network. The proposed model can act on a broad spectrum of hazards, as it uses general features to compute the effects of the hazard on the evacuation. Furthermore, the model structure enables the assessment of various categories of evacuation, ranging from voluntary over recommended to mandatory. And, the behavioural responses of the evacuees towards evacuation instructions are modelled, such that instructions can be followed fully, followed in part, or rejected completely. An illustrative example of a hypothetical evacuation shows the principles and possibilities of the posed evacuation model

Generalized Multivariate Extreme Value Models for Explicit Route Choice Sets

This paper analyses a class of route choice models with closed-form probability expressions, namely, Generalized Multivariate Extreme Value (GMEV) models. A large group of these models emerge from different utility formulas that combine systematic utility and random error terms. Twelve models are captured in a single discrete choice framework. The additive utility formula leads to the known logit family, being multinomial, path-size, paired combinatorial and link-nested. For the multiplicative formulation only the multinomial and path-size weibit models have been identified; this study also identifies the paired combinatorial and link-nested variations, and generalizes the path-size variant. Furthermore, a new traveller's decision rule based on the multiplicative utility formula with a reference route is presented. Here the traveller chooses exclusively based on the differences between routes. This leads to four new GMEV models. We assess the models qualitatively based on a generic structure of route utility with random foreseen travel times, for which we empirically identify that the variance of utility should be different from thus far assumed for multinomial probit and logit-kernel models. The expected travellers' behaviour and model-behaviour under simple network changes are analysed. Furthermore, all models are estimated and validated on an illustrative network example with long distance and short distance origin-destination pairs. The new multiplicative models based on differences outperform the additive models in both tests

Including passengers’ response to crowding in the Dutch national train passenger assignment model

Transit passengers’ response to crowded conditions has been studied empirically, yet is limitedly included in transport models currently used in the design of policy and infrastructure investments. This has consequences for the practical applicability of these models in studies on, for instance, timetabling, train capacity management strategies, project appraisal, and passenger satisfaction. Here we propose four methods to include the effect of crowding, based on existing studies on passengers’ perception and response as well as often-used crowding indicators. These four alternative methods are implemented in the train passenger assignment procedure of the Dutch national transport model, and evaluated with respect to their impacts on the model results for the Dutch railway network. The four methods relate to four different ways in which an additive trip penalty and/or time-multiplier can be incorporated in the train utility function for different travel purposes, to capture the disutility of crowding as measured by the load factor. The analyses of the test case favor the hybrid method using both a boarding penalty (capturing seat availability upon boarding) and a time-multiplier (capturing physical comfort and safety throughout the trip). This method produces consistent results, while the additional computational effort that it imposes is acceptable. Further empirical underpinning is needed to conclusively show which of these methods best captures passengers’ response behavior quantitatively (for different travel purposes and conditions)

Analytically Derived Versus Numerically Derived Urban Transit Guidelines Case Study of Utrecht, Netherlands

Urban transit guidelines yield optimal network characteristics relating to factors such as stop density, network density, service frequency, and network hierarchy. These guidelines are typically derived from a simple analytical formulation of the bilevel network design problem. This optimization problem can be solved analytically only when simplifying assumptions are made regarding the demand distribution, local constraints, and travel behavior. The primary objective of this paper is to verify the validity of the assumptions underlying analytically derived guidelines. To this end, a new numerical optimization tool is developed and applied to the design of several detailed topological transit networks (including routes and line-specific properties) for the medium-sized Dutch city of Utrecht, Netherlands. Average network characteristics are derived, and a comparison is made between the existing analytically derived guidelines, the new numerically derived optimal characteristics, and the characteristics of the present network. Both analytically derived and numerically derived guidelines recommend lower stop densities, coarser networks, and higher frequencies than those found in real life. Furthermore, the introduction of zone lines would improve network quality, while express lines are beneficial only for large demand concentrations. Although existing (typically analytically derived) guidelines are found to be in line with numerically derived guidelines, the former are less suitable because local constraints are not accounted for, and only average network characteristics are computed. Therefore, the detailed network designs that are numerically derived in this study are easier to implement

Marginal cost congestion pricing based on the network fundamental diagram

ISSN:0968-090

Determinants of Bicycle Transfer Demand at Metro Stations Analysis of Stations in Nanjing, China

Metro transit networks are constantly expanding to meet the growing travel demand that accompanies rapid urban growth. One main disadvantage of metro transport is its heavy reliance on access and egress transport and, hence, on the corresponding transfer facilities. Improvements to these transfer facilities therefore have the potential to increase use of the metro system while possibly alleviating the traffic load on the urban road network. Because bicycle transfer at metro stations—that is, using a bicycle as mode of access to or egress from the metro system—is underused, the determinants of the demand for bicycle transfer are investigated. Results and findings are valuable for designing policies aimed at improving metro ridership and for designing bicycle parking and transfer facilities at metro stations. To this end, several metro stations in Nanjing, China, were analyzed, and two stereotypical metro stations were selected for how well they represented the system. A large-scale survey was conducted on metro travelers\u27 opinions on and use of bicycle transfer facilities, and data were collected on the current attributes of service groups, bicycle parking occupancy, and transfer mode alternatives. Furthermore, metro travelers\u27 (latent) transfer preferences for bicycle rental facilities were investigated. Two transfer choice models were estimated to identify and quantify the determinants for bicycle transfer demand: one focuses on current walk-metro trips, and the other focuses on current bus-metro trips. The explanatory determinants are discussed, and relative weights are computed with multiple linear regression analysis