Stated choices and simulated experiences: Differences in the value of travel time and reliability

Surveys with stated choice experiments (SCE) are widely used to derive values of time and reliability for transport project appraisal purposes. However, such methods ask respondents to make hypothetical choices, which in turn could create a bias between choices made in the experiment compared to those in an environment where the choices have consequence. In this paper, borrowing principles of experimental economics, we introduce an incentive compatible driving simulator experiment, where participants are required to experience the travel time of their chosen route and actually pay any toll costs associated with the choice of a tolled road. In a first for the literature, we use a within respondent design to compare both the value of travel time savings (VTT) and value of travel time reliability (VOR) across a typical SCE and an environment with simulated consequence. Given the importance of VTT and VOR to transport decision making and the difficulty in estimating VOR using revealed preference data, our results are noteworthy and emphasise that more research on this topic is imperative. We provide suggestions on how the results herein may be used in future studies, to potentially reduce hypothetical bias that may be exhibited in SCE

Using Scaling Methods to Improve Support Vector Regression’s Performance for Travel Time and Volume Predictions

Long queues often happen on toll roads, especially at the tollgates. These create many problems, including having an impact on the regular roads nearby. If travel time and traffic volume at the tollgates can be predicted accurately in advance, this would allow traffic authorities to take appropriate measures to improve traffic flow and the safety of road users. This paper describes a novel combination of scaling methods with Support Vector Machines for Regression (SVR) for travel time and tollgate volume prediction tasks, as part of the Knowledge Discovery and Data Mining (KDD) Cup 2017. A new method is introduced to handle missing data by utilising the structure of the road network. Moreover, experiments with reduced data were conducted to evaluate whether conclusions from combining scaling methods with SVR could be generalised

Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing

Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, and efficient traffic-speed prediction for large-scale transportation networks where numerous traffic detectors are deployed has not been well studied. In this paper, we propose DistPre, which is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. To achieve fine-grained and accurate traffic-speed prediction, DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate hyperparameter configuration for a detector. To make such customization process efficient and applicable for large-scale transportation networks, DistPre conducts LSTM customization on a cluster of computation nodes and allows any trained LSTM model to be shared between different detectors. If a detector observes a similar traffic pattern to another one, DistPre directly shares the existing LSTM model between the two detectors rather than customizing an LSTM model per detector. Experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistPre. The results demonstrate that DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.Comment: 14 pages, 7 figures, 2 tables, Euro-par 2020 conferenc

Fusing heterogeneous and unreliable data from traffic sensors

Fusing traffic data from a variety of traffic sensors into a coherent, consistent, and reliable picture of the prevailing traffic conditions (e.g. densities, speeds, flows) is a critical and challenging task in any off- or online traffic management or information system which use these data. Recursive Kalman filter-based approaches provide an intuitive and powerful solution for traffic state estimation and data fusion, however, in case the data cannot be straightforwardly aligned over space and time, the equations become unwieldy and computationally expensive. This chapter discusses three alternative data fusion approaches which solve this alignment problem and are tailored to fuse such semantically different traffic sensor data. The so-called PISCIT and FlowResTD methods both fuse spatial data (individual travel times and low-resolution floating car data, respectively) with a prior speed map obtained from either raw data or another estimation method. Both PISCIT and FlowResTD are robust to structural bias in those a priori speeds, which is critically important due to the fact that many real-world local sensors use (arithmetic) time averaging, which induces a significant bias. The extended and generalized Treiber–Helbing filter (EGTF) in turn is able to fuse multiple data sources, as long as for each of these it is possible to estimate under which traffic regime (congested, free flowing) the data were collected. The algorithms are designed such that they can be used in a cascaded setting, each fusing an increasingly accurate posterior speed map with new data, which in the end could be used as input for a model-based/Kalman filter approach for traffic state estimation and prediction

Lagrangian formulation of a multi-class kinematic wave model

The kinematic wave model is often used in simulation tools to describe dynamic traffic flow and to estimate and predict traffic states. Discretization of the model is generally based on Eulerian coordinates, which are fixed in space. However, the Lagrangian coordinate system, where the coordinates move with the velocity of the vehicles, results in more accurate solutions. Furthermore, if the model includes multiple user classes it describes real traffic more accurately. Such a multi-class model, in contrast to a mixed-class model, treats different types of vehicles (eg. passenger cars and trucks or vehicles with different origins and/or destinations) differently. We combine the Lagrangian coordinate system with a multi-class model and propose a Langrangian formulation of the kinematic wave model for multiple user classes. We show that the advantages of the Lagrangian formulation also apply for the multi-class model. Simulations based on the Lagrangian formulation result in more accurate solutions than simuations based on the Eulerian formulation