The Demand Model of the Danish National Model

In the present paper the structure of the demand model framework for the new Danish national model is outlined. It involves a discussion at two levels. At the framework level, we discuss how demand is modelled in a number of parallel sub-models and how these are linked. An issues is to represent all transport but on the other hand avoid double counting. At the sub-model level, special attention is given to the week-day model. As the model for week-day traffic are the most important components with respect to overall mileage and congestion it is particular important from a policy point of view. We discuss in more details the design of tours (trip chains) and how these will be organised into primary and secondary tours with intermediate stop activities. Finally, we discuss how activity chains can be represented in a matrix form prior to the assignment procedure

The Demand Model of the Danish National Model

In the present paper the structure of the demand model framework for the new Danish national model is outlined. It involves a discussion at two levels. At the framework level, we discuss how demand is modelled in a number of parallel sub-models and how these are linked. An issue is to represent all transport but on the other hand avoid double counting. At the sub-model level, special attention is given to the week-day model as well as the international component of the model. The model for week-day traffic represents the most important components with respect to overall mileage and congestion and is therefore particularly important from a policy point of view. The international models are relevant because these are central to the Fehmarn Belt application of the model to be finished in the first quarter of 2012. For the international models we consider segmentation and nesting structure for the international day model, the transit model, and the overnight model. Finally, we discuss how trip chains of higher dimensions can be represented in a matrix form applicable for the assignment procedure

Modelling modal-split and trip length in a simultaneously discrete-continuous setting

Econometrics models for traffic behaviour as modal-split, car ownership, travel purpose, etc. is often based on the assumption that individual behaviour can be decomposed into a finite set of discrete choice. However, in some circumstances, for instance in modelling actual demand measured as trip length, this approach fails to fit reality. This paper present a simultaneous model set-up for the joint decision of modal-split and trip length being continuous. The model is estimated in a general maximum likelihood framework and is based on a stratified sample for Aarhus, the second largest city in Denmark

Scalable Population Synthesis with Deep Generative Modeling

Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for how to 'grow' pools of micro-agents is presented. The model framework adopts a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE). Compared to the previous population synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs sampling and traditional generative models such as Bayesian Networks or Hidden Markov Models, the proposed method allows fitting the full joint distribution for high dimensions. The proposed methodology is compared with a conventional Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary. It is shown that, while these two methods outperform the VAE in the low-dimensional case, they both suffer from scalability issues when the number of modeled attributes increases. It is also shown that the Gibbs sampler essentially replicates the agents from the original sample when the required conditional distributions are estimated as frequency tables. In contrast, the VAE allows addressing the problem of sampling zeros by generating agents that are virtually different from those in the original data but have similar statistical properties. The presented approach can support agent-based modeling at all levels by enabling richer synthetic populations with smaller zones and more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table

System convergence in transport models: algorithms efficiency and output uncertainty

Transport models most often involve separate models for traffic assignment and demand. As a result, two different equilibrium mechanisms are involved, (i) the internal traffic assignment equilibrium, and (ii) the external equilibrium between the assignment model and the demand model. The objective of this paper is to analyse convergence performance for the external loop and to illustrate how an improper linkage between the converging parts can lead to substantial uncertainty in the final output. Although this loop is crucial for the performance of large-scale transport models it has not been analysed much in the literature. The paper first investigates several variants of the Method of Successive Averages (MSA) by simulation experiments on a toy-network. It is found that the simulation experiments produce support for a weighted MSA approach. The weighted MSA approach is then analysed on large-scale in the Danish National Transport Model (DNTM). It is revealed that system convergence requires that either demand or supply is without random noise but not both. In that case, if MSA is applied to the model output with random noise, it will converge effectively as the random effects are gradually dampened in the MSA process. In connection to DNTM it is shown that MSA works well when applied to travel-time averaging, whereas trip averaging is generally infected by random noise resulting from the assignment model. The latter implies that the minimum uncertainty in the final model output is dictated by the random noise in the assignment model

A long-distance travel demand model for Europe

In Europe, approximately 50% of all passenger kilometres come from trips beyond 100 km according to matrices developed in the TRANSTOOLS project. This accounts for an even larger share of CO2 emissions due to a higher modal share of air transport. Therefore long-distance trips are increasingly relevant from a political and environmental point of view. The paper presents the first tour-based long-distance travel demand model for passenger trips in and between 42 European countries. The model is part of a new European transport model developed for the European Commission, the TRANSTOOLS II model, and will serve as an important tool for transport policy analysis at a European level. The model is formulated as a nested logit model and estimated based on travel diary data with segmentation into business, private, and holiday trips. We analyse the estimation results and present elasticities for a number of different level-ofservice variables. The results suggest that the perception of both travel time and cost varies with journey length in a non-linear way. For car drivers and car passengers, elasticities increase with the length of the journey, whereas the opposite is true for rail, bus, and air passengers – a fact that reflects a change in substitutability. Moreover, elasticities differ significantly by trip purpose with private trips having the highest and holiday trips the lowest elasticities

System convergence in transport models: algorithms efficiency and output uncertainty

Transport models most often involve separate models for traffic assignment and demand. As a result, two different equilibrium mechanisms are involved, (i) the internal traffic assignment equilibrium, and (ii) the external equilibrium between the assignment model and the demand model. The objective of this paper is to analyse convergence performance for the external loop and to illustrate how an improper linkage between the converging parts can lead to substantial uncertainty in the final output. Although this loop is crucial for the performance of large-scale transport models it has not been analysed much in the literature. The paper first investigates several variants of the Method of Successive Averages (MSA) by simulation experiments on a toy-network. It is found that the simulation experiments produce support for a weighted MSA approach. The weighted MSA approach is then analysed on large-scale in the Danish National Transport Model (DNTM). It is revealed that system convergence requires that either demand or supply is without random noise but not both. In that case, if MSA is applied to the model output with random noise, it will converge effectively as the random effects are gradually dampened in the MSA process. In connection to DNTM it is shown that MSA works well when applied to travel-time averaging, whereas trip averaging is generally infected by random noise resulting from the assignment model. The latter implies that the minimum uncertainty in the final model output is dictated by the random noise in the assignment model

How adaptive cruise control systems may increase congestion: An MFD perspective

Det er tiltalende at tro, at adaptive fartpiloter (ACC), som er forløberen for autonome køresystemer, vil lede til mindre trængsel ved at muliggøre en mere effektiv kørsel. I papiret undersøger vi denne hypotese ved at sammenligne tidssikkerhedsintervallet for ACC-systemer (på tværs af bilmærker) med observerede tidsintervaller for menneskelige chauffører. Ved at samle tidssikkerhedsintervallet i et fundamentalt makroskopisk diagram (MFD) af en stor dansk motorvej i morgenmyldretiden, konkluderes det, at; i) menneskelige chauffører opretholder et markant lavere tidsinterval i sammenligning med det underforståede gennemsnitlige sikkerhedsinterval for ACC-aktiverede biler, og ii) det lavere tidsinterval er effektivt fra et MFD-perspektiv. Med ACC-teknologien som den er i dag, og ved at anvende standardindstillinger, vil øget brug af ACC sandsynligvis bidrage til mere trængsel. I papiret diskuteres konsekvenser samt mulige initiativer til at imødegå effekten.It is appealing to believe that adaptive cruise control (ACC) systems, the forerunner of autonomous driving systems, will provide congestion relief by allowing for more efficient driving. In the paper, we investigate this hypothesis by comparing the time safety gap of ACC systems (across manufactures) with the observed revealed safety gap of human drivers. By clustering the safety gap within a network macroscopic fundamental diagram (MFD) of a large Danish motorway in the morning peak, it is concluded that; i) human drivers maintain a significantly lower safety gap when compared to the implied average safety gap of ACC enabled cars, and ii) the lower safety gap is efficient from an MFD perspective. Hence, with the ACC technology state of today and by applying standard settings, increased use of ACC is likely to contribute to more congestion. In the paper, we discuss possible consequences and initiatives that might help mitigating the effect