Influence of priority taking and abstaining at single-lane roundabouts using cellular automata

Existing roundabout simulation models fail to consider all types of driver behavior which compromises their accuracy and ability to accurately evaluate roundabout performance. Further, these non-compliant driver behaviors, including priority taking and priority abstaining, are inconsistent with existing traffic flow theories. In this paper, a new cellular automata model, C.A.Rsim, is developed and calibrated with field data from five single-lane roundabouts in four northeastern states. Model results indicate that approximately 20% of the individuals in the driver population are inclined to priority taking and approximately 20% are inclined to priority abstaining behavior, though the observed levels of these types of behavior are naturally lower and vary with traffic volume. The model results also corroborate other research indicating that current models can overestimate capacity at higher circulating volumes, possibly a result of the jamming effect produced by priority taking behavior. The reduction in priority abstaining behavior, which is observed at older roundabouts, significantly reduces delay and queue length in certain traffic volumes. C.A.Rsim is also more parsimonious than many existing microsimulation models. These results provide insight on how variations in conflicting flow (i.e., traffic volume and turning movement balance) impact the amount of observed non-compliant behavior.     

Impacts of trade related sustainability strategies on freight transportation: Modelling framework and application for France

The circular and functional economies are being presented in the literature as potential strategies for future sustainable societies. In terms of the consequences for supply chains, they will promote a much more dispersed and diversified, local and network based usage of goods than the current economy, which is comparatively linear, concentrated, long distance oriented and scale economy based. A gap in the literature is the assessment of the effects of these systems on freight transport flows. In our paper, we present a first attempt at estimating this impact using freight transport scenario building and quantitative modelling. In order to translate the main parameters that characterize these systems into factors determining freight transportation volumes, we develop a framework based on a typology of goods categories describing functional and spatial proximity between producers and consumers. In order to simulate changes in the economy, we develop scenarios for the shifting of goods from one category to another and, additionally, include internalization policies that should guide their realization. We calculate the impacts on freight flows using a new interregional transport model for France that includes distribution chains and produces estimates of external costs of transport. Our results show that circular and functional economies could lead to a 2–5% reduction of air pollutant emissions and up to a 14–26% reduction if combined with the internalization of external costs. The scenario with ongoing mass production for differentiated demand is found to lead to a 5% increase of environmental impacts compared to the baseline.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

A mixed logit model for predicting exit choice during building evacuations

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns is the escape route. The choice of a route may involve local decisions on alternative exits from an enclosed environment. This study investigates the effect of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1503 participants is obtained and a mixed logit model is calibrated using these data. The model shows that the presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model indicates that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main aim of this study is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.     

Eco-driver training within the City of Calgary’s municipal fleet: Monitoring the impact

This article highlights eco-driving as an available policy option to reduce climate altering GHG emissions. Recognizing the need to reduce the environmental impact of its fleet operations, the City of Calgary is a leader in developing programs and policies that aim to reduce GHG emissions and associated pollutants resulting from the use of fossil fuels. Among local action taken against climate change, the City sought to quantify CO₂ emissions reductions from their municipal fleet as a result of eco-driver training, with a specific focus on engine idling. Fifteen drivers from the Development & Building Approvals Business Unit had in-vehicle monitoring technology (CarChips®) installed into their vehicles as part of a three-phase research process. The results show that gasoline and hybrid vehicles decreased average idling between 4% and 10% per vehicle per day, leading to an average emissions decrease of 1.7 kg of CO₂ per vehicle per day.     

Altimeter assimilation in the OCCAM global model: Part I: A twin experiment

An altimeter data assimilation scheme has been tested in the OCCAM (Ocean Circulation and Climate Advanced Modelling) global 1/4°, 36-level model using a twin experiment format. The Cooper and Haines displacement scheme is used. The method works well in most regions and depths. Currents and densities in the top 1000 m generally improve by over 50–70% after 5 months of sea level assimilation every 15 days. Below 1000 m, an error reduction of up to 50% is achieved. The errors remain low during a further 60-day run without assimilation. Diagnostics for the North Atlantic, the Tropical Pacific and the Antarctic Circumpolar Current are shown alongside the global averages.The main problems encountered were in weakly stratified regions of the Antarctic and Arctic seas. A scale selective filter is developed to avoid assimilating scales much larger than the local deformation radius, and this avoids the adverse assimilation effects in the southern oceans. A companion paper uses this scheme to assimilate TOPEX and ERS-1 altimeter maps.     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.     

Evaluation of the effectiveness of accident information on freeway changeable message signs: A comparison of empirical methodologies

In this paper, we study the effectiveness of accident messages that are displayed on freeway changeable message signs (CMS). Motivated by the lack of empirical studies and the mixed results reported in the limited empirical studies, this paper focuses on the comparison of different aggregate analysis methodologies and their corresponding results, using the same empirical data set. We have two major findings. First, we find that the CMS accident messages do not seem to have any significant immediate effect on driver diversion based on our empirical data. Visible congestion, on the other hand, seems to be an important factor for driver diversion. Second, we show how the conclusion could have been if wrong methodologies were to be adopted. Methods that rely on correlation alone but not the timing of events (what we call correlation method) yield high correlation between CMS accident messages and driver diversion, which is typically and incorrectly interpreted as CMS accident messages being effective.     

The performance of route modification and demand stabilization strategies in stochastic vehicle routing

A key concern in managing vehicle routing operations under stochastic demands is whether, on the basis of travel distance, route modification yields materially greater logistical efficiency than fixed routes. This research uses statistical calibration as the primary technique to develop a robust and tractable model for estimating this difference in logistical efficiency. Based on features such as the models predictive accuracy and generalizability, it constitutes a substantive improvement over existing models. The present study also expands the range of predictive models relevant to vehicle routing under stochastic demands with models to estimate the transportation and inventory effects of persuading customers to stabilize their ordering patterns.     

Investigations of vehicle securing without lashings for Ro-Ro ships

To reduce the area on deck occupied by each car and the labour and time required for lashing and unlashing cars, it has been proposed by the author that in some conditions, cars on decks could be transported without lashing. To evaluate cargo securing based on this “lashing-free” concept, computational code was developed to calculate the vehicle–deck interactions for various ship motions. The code was structured by incorporating a vehicle model into a piece of ship motion calculation code. A series of time-domain simulations were conducted to evaluate vehicle securing. It was found that for a target ship consisting of a 6000-unit Ro-Ro vessel, vehicle securing is mainly influenced by the ship's rolling motions and is highly dependent on the wave height and loading conditions. It was suggested from the analysis that vehicles could be secured without being lashed in a large area of the ship in specific weather conditions and on some routes with less adverse sea states. However, it is still suggested that conventional lashing holes should be constructed on the deck because in severe sea states the cars will still need to be lashed. The limitations of the current investigations are also presented. The relevant research work was carried out at the Division of Ship Design and Engineering, Department of Shipping and Marine Technology, Chalmers University of Technology.