Continuous-time general link transmission model with simplified fanning, Part II: Event-based algorithm for networks

In this paper a novel solution algorithm is proposed for solving general first order dynamic network loading (DNL) problems in general transport networks. This solution algorithm supports any smooth non-linear two regime concave fundamental diagram and adopts a simplified fanning scheme. It is termed eGLTM (event-based General Link Transmission Model) and is based on a continuous-time formulation of the kinematic wave model that adapts shockwave theory to simplify expansion fans. As the name suggests eGLTM is a generalisation of eLTM, which is a special case that solves the simplified first order model assuming a triangular fundamental diagram. We analyse the impact of modelling delay in the hypocritical branch of the fundamental diagram to assess the differences between the two models. In addition, we propose an additional stream of mixture events to propagate multi-commodity flow in event based macroscopic models, which makes both eLTM and eGLTM suitable for dynamic traffic assignment (DTA) applications. The proposed solution scheme can yield exact solutions as well as approximate solutions at a significantly lesser cost. The efficiency of the model is demonstrated in a number of case studies. Furthermore, different settings for our simplified fanning scheme are investigated as well as an extensive analysis on the effect of including route choice on the algorithms computational cost. Finally, a large scale case study is conducted to investigate the suitability of the model in a practical context and assess its efficiency compared to the simplified first order model

General solution scheme for the Static Link Transmission Model

Until the present day most static traffic assignment models are neither capacity constrained nor storage constrained. Recent studies have resulted in novel approaches that consider capacity constraints and sometimes storage constraints. We build upon the results of these works and the model formulated in our companion paper Bliemer and Raadsen (2018a) which comprises a static assignment model formulation that is both capacity constrained as well as storage constrained. The formulation of this model is derived from a continuous time dynamic network loading model proposed in Bliemer and Raadsen (2018b). The prospect of being able to capture spillback effects in static assignment provides new opportunities for making this modelling method more capable. It is well known that the absence of spillback typically results in significant underestimation of path travel times. This is especially true for paths that do not traverse bottleneck(s) directly, but that are affected by the space occupied of queues that are spilling back. Similar to Smith (2013) and Smith et al. (2013), Bliemer and Raadsen (2018a) did not provide a solution algorithm. In this paper, we take their model formulation and propose a general solution scheme suitable for large scale networks

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

Construction of experimental designs for mixed logit models allowing for correlation across choice observations

In each stated choice (SC) survey, there is an underlying experimental design from which the hypothetical choice situations are determined. These designs are constructed by the analyst, with several different ways of constructing these designs having been proposed in the past. Recently, there has been a move from so-called orthogonal designs to more efficient designs. Efficient designs optimize the design such that the data will lead to more reliable parameter estimates for the model under consideration. The main focus has been on the multinomial logit model, however this model is unable to take the dependency between choice situations into account, while in a stated choice survey usually multiple choice situations are presented to a single respondent. In this paper, we extend the literature by focusing on the panel mixed logit (ML) model with random parameters, which can take the above mentioned dependency into account. In deriving the analytical asymptotic variance-covariance matrix for the panel ML model, used to determine the efficiency of a design, we show that it is far more complex than the crosssectional ML model (assuming independent choice observations). Case studies illustrate that it matters for which model the design is optimized, and that it seems that a panel ML model SC experiment needs less respondents than a cross-sectional ML experiment for the same level of reliability of the parameter estimates

Confidence intervals of willingness-­‐to-­‐pay for random coefficient logit models

Random coefficient logit (RCL) models containing random parameters are increasingly used for modelling travel choices. Willingness-to-pay (WTP) measures, such as the value of travel time savings (VTTS) are, in the case of such RCL models, ratios of random parameters. In this paper we apply the Delta method to compute the confidence intervals of such WTP measures, taking into account the variancecovariance matrix of the estimates of the distributional parameters. Compared to simulation methods such as proposed by Krinsky and Robb, the Delta method is able to avoid some of the simulations by deriving partly analytical expressions for the standard errors. Examples of such computations are shown for different combinations of random distributions

Rewarding instead of charging road users: a model case study investigating effects on traffic conditions

Instead of giving a negative incentive such as transport pricing, a positive incentive by rewarding travelers for ‘good behavior’ may yield different responses. In a Dutch pilot project called Peak Avoidance (in Dutch: “SpitsMijden”), a few hundred travelers participated in an experiment in which they received 3 to 7 euros per day when they avoided traveling by car during the morning rush hours (7h30–9h30). Mainly departure time shifts were observed, together with moderate mode shifts. Due to the low number of participants in the experiment, no impact on traffic conditions could be expected. In order to assess the potential of such a rewarding scheme on traffic conditions, a dynamic traffic assignment model has been developed to forecast network wide effects in the long term by assuming higher participation levels. This paper describes the mathematical model. Furthermore, the Peak Avoidance project is taken as a case study and different rewarding strategies with varying participation levels and reward levels are analyzed. First results show that indeed overall traffic conditions can be improved by giving a reward, where low to moderate reward levels and participation levels of 50% or lower are sufficient for a significant improvement. Higher participation and reward levels seem to become increasingly counter-effective

Extended Macroscopic Node Model for Multilane Traffic

In a macroscopic assignment model, traffic flows are distributed onto the network by means of a network loading model. The network loading propagates flows along links via a link model and through junctions or intersections via a node model. Most of the travel time delays are caused by queues forming at junctions or intersections, especially in urban networks. Therefore, the efficiency and accuracy of the underlying node model is paramount in capturing these delays (and flows). Existing link-based macroscopic node models make the simplifying assumption that first-in-first-out (FIFO) holds at the link level, which is often unrealistic when a link has multiple approach lanes near an intersection or junction. In this work we propose to relax this assumption such that FIFO holds at the movement level. We do so by developing several model extensions. First, a novel lane-based formulation of the node model is proposed. Secondly, we formulate an equilibrium problem and a general solution algorithm to allocate sending flows to lanes. This allows us to explicitly consider approach lane configurations that contain important information about the layout of an intersection or junction. We show that the conventional link-based node model is a special case of our newly proposed model in case each approach lane on an incoming link allows all possible movements. Various numerical examples are provided, demonstrating the capabilities of the proposed extensions to the node model

Comparison of Different Toll Policies in the Dynamic Second-best Optimal Toll Design Problem: Case study on a Three-link network

In this paper, the dynamic optimal toll design problem is considered as a one leader-many followers hierarchical non-cooperative game. On a given network the road authority as the leader tolls some links in order to reach its objective, while travelers as followers minimize their perceived travel costs. So far toll has always been considered either as constant or as time-varying. Inspired by the San Diego's Interstate 15 congestion pricing project, in which heuristics with toll proportional to traffic flow are applied on a real two-link highway network, we consider toll as proportional to traffic flows in the network. On a three-link network we investigate various toll schemes and their influence on the outcome of the game for the road authority. We show that the use of alternative toll schemes may improve system performance remarkably

Incorporating model uncertainty into the generation of efficient stated choice experiments: A model averaging approach

Stated choice (SC) studies typically rely on the use of an underlying experimental design to construct the hypothetical choice situations shown to respondents. These designs are constructed by the analyst, with several different ways of constructing these designs having been proposed in the past. Recently, there has been a move from so-called orthogonal designs to more efficient designs. Efficient designs optimize the design such that the data will lead to more reliable parameter estimates for the model under consideration. The literature dealing with the generation of efficient designs has examined and largely solved the issue of a requirement for a prior knowledge of the parameter estimates that will be obtained post data collection. Nevertheless, problems related to the fact that the efficiency of a SC experiment is related to the variance-covariance matrix of the model to be estimated and that different econometric models will have different variance-covariance matrix, thus resulting in different levels of efficiency for the same design, has yet to be addressed. In this paper, we propose the use of a model averaging process over different econometric models to solve this problem. Via the use of a case study, we show that designs generated using the model averaging process prove robust to different model estimation as well as provide decent levels of protection against biased parameter estimates relative to designs generated specifically for a given model type