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The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an "iterative route cover optimization method" to suggest charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. [ABSTRACT FROM AUTHOR]

High productivity in forest harvesting requires efficient forwarding. Planning is complicated by multiple choices of routes and their order, the number and types of assortments, the loading sequence, and pile organization at the landing. This paper develops and tests a decision support tool for forwarder routing with sequential co-loading of assortments. Input data are the harvester production file (including Global Navigation Satellite Systems tracking), placement of landing, and machine specifications. The trail network is generated from the harvester production data when devising routes to pick up all log piles, including specific assortments and volumes. Multiple assortments can be loaded along each route, and a certain loading sequence of assortments is preferred and (or) required. Sorting time during co-loading varies, depending on the assortment combinations and bunk loading pattern. Route planning is modeled using a set-partitioning problem, and the solution method is a metaheuristic based on repeated matching. In addition to routes and loading sequences, solutions include the organization of assortment piles at the landing, depending on the total volume of each assortment. The tool produces similar results to those attained by skilled forwarder operators on five clearcuts (3–11 ha) in northern Sweden, when the results are compared with data from actual forwarder production files. [ABSTRACT FROM AUTHOR]

In recent decades, there has been a decline in active school transportation (AST). Parents play an important role as the key decision-makers of children's AST, and there is a need of more knowledge about the decision-making process and parents' beliefs towards AST. The overall aim of this study was to explore parents' attitudes and beliefs towards AST in the northern part of Sweden. Twenty parents participated in semi-structured interviews, which was based on the theory of planned behaviour. Qualitative content analysis was used to analyse the interviews. The analysis yielded one main theme, "Parenting and active school transportation – making route choices in a changed landscape" and four subthemes, "Knowing that it is beneficial while struggling with daily life", "Considering barriers and solutions to enable AST", "Parenting is challenging and about balancing", and "Reflecting and contemplating about what we and others do". Winter conditions affect parents' decisions, and this needs to be considered when facilitating AST in these regions. Overall better health, increased physical activity, time spent outdoors, and free play were revealed as positive outcomes of AST. Decisions were also influenced by social norms and how the idea of parenting has changed through generations. The findings of this study are likely to be important when promoting AST. [ABSTRACT FROM AUTHOR]

• A latent-class, latent-choice, latent-path adaptive route choice model is specified to address three major challenges in estimating routing policy choice models in real-life networks. • A case study is conducted in Stockholm, Sweden and data for the stochastic time-dependent network are generated from hired taxi Global Positioning System (GPS) readings. • Routing policy choice set generation has achieved 95% coverage for 100% overlap threshold after correcting GPS mistakes and breaking up trips with intermediate stops, and further achieve 100% coverage for 90% overlap threshold. • Estimation results show that the routing policy user class probability increases with trip length, and the latent-class routing policy choice model fits the data better than a single-class path choice or routing policy choice model. Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating real-time traveler information systems and related policies. This research contributes to the state of the art by developing a latent-class routing policy choice model in a stochastic time-dependent network with revealed preference data. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler's ability to look ahead in order to incorporate real-time information not yet available at the time of decision. A case study is conducted in Stockholm, Sweden and data for the stochastic time-dependent network are generated from hired taxi Global Positioning System (GPS) readings. A latent-class Policy Size Logit model is specified, with routing policy users who follow routing policies and path users who follow fixed paths. Two additional layers of latency in the measurement equation are accounted for: 1) the choice of a routing policy is latent and only its realized path on a given day can be observed; and 2) when GPS readings have relatively long gaps, the realized path cannot be uniquely identified, and the likelihood of observing vehicle traces with non-consecutive links is instead maximized. Routing policy choice set generation is based on the generalization of path choice set generation methods. The generated choice sets achieve 95% coverage for 100% overlap threshold after correcting GPS mistakes and breaking up trips with intermediate stops, and further achieve 100% coverage for 90% overlap threshold. Estimation results show that the routing policy user class probability increases with trip length, and the latent-class routing policy choice model fits the data better than a single-class path choice or routing policy choice model. This suggests that travelers are heterogeneous in terms of their ability and/or willingness to plan ahead and utilize real-time information, and an appropriate route choice model for uncertain networks should take into account the underlying stochastic travel times and structured traveler heterogeneity in terms of real-time information utilization. [ABSTRACT FROM AUTHOR]

Abstract: When using random utility models for a route choice problem, a critical issue is the significant correlation among alternatives. There are basically two types of models proposed in the literature to address it: (i) a deterministic correction of the path utilities in a Multinomial Logit model (such as the Path Size Logit or the C-Logit models) and (ii) an explicit modeling of the correlation through assumptions about the error terms, and the use of advanced discrete choice models such as the Cross-Nested Logit or the Error Component models. The first is simple, easy to handle and often used in practice. Unfortunately, it does not correctly capture the correlation structure, as we discuss in details in the paper. The second is more consistent with the modeling objectives, but very complicated to specify and estimate. The modeling framework proposed in this paper allows the analyst to control the trade-off between the simplicity of the model and the level of realism. Within this framework, the key concept capturing the correlation structure is called a subnetwork. A subnetwork is a simplification of the road network only containing easy identifiable and behaviorally relevant roads. In practice, the subnetwork can easily be defined based on the route network hierarchy. The importance and the originality of our approach lie in the possibility to capture the most important correlation without considerably increasing the model complexity. This makes it suitable for a wide spectrum of applications, namely involving realistic large-scale networks. As an illustration, we present estimation results of a factor analytic specification of a mixture of Multinomial Logit model, where the correlation among paths is captured by error components. The estimation is based on a GPS dataset collected in the Swedish city of Borlänge. The results show a significant increase in model fit and forecasting performance for the Error Component model compared to a Path Size Logit model. Moreover, the correlation parameters are significant. [Copyright &y& Elsevier]

Combined network equilibrium models of trip distribution, modal split, and route choice are formulated and estimated for the Stockholm region. These models take into account the feedback effects among three stages of the classical four-step model. A simultaneous structure of the mode and destination choices is studied together with nested combined models reflecting conditional choice probabilities. In the traditional combined model, mode choice is assumed conditional on destination choice. The reverse combined model, in contrast, considers destination choice conditional on mode choice. User-optimal traffic assignment is integrated with these approaches to modeling travel demand in network equilibrium models. The three combined models are estimated using the full-information maximum likelihood technique implying that all preference parameters are estimated simultaneously. The models are estimated on medium-size travel and network data for the Stockholm region in 1986. The results suggest a rejection of the traditional nested model because of incorrect relative values of the estimated cost-sensitivity parameters. The reverse nested model is preferred, even if the overall goodness-of-fit is better for the traditional nested model. An application to a future scenario for the year 2020 is included. [ABSTRACT FROM AUTHOR]

The aim of the work presented here is to develop a mathematical model that can assist decision-makers in selecting the optimal network of subsidised routes in air transportation. Sweden is used as a case study. The results show that even if most of the Swedish population already has a good accessibility, the model can suggest new routes that can further improve accessibility without increasing the subsidisation cost. The closure of an airport reduces the available commercial routes, which would impair the accessibility to a given destination; a subsidised route would hence be required as a replacement. The ability of the model to consider several accessibility criteria makes it useful for managers at transportation authorities when making airport location decisions. • A decision support tool for suggesting and evaluating subsidised flight networks is developed. • A budget-constrained maximum coverage model is used. • The model maximises the number of people served with regard to multiple accessibility criteria. • A case study for Sweden suggests that it is possible to improve the network without increasing the subsidisation cost. [ABSTRACT FROM AUTHOR]