Measurement uncertainty determination and curve-fitting algorithms for development of accurate digital maps for advanced driver assistance systems

Digital maps can provide support for numerous advanced driver assistance systems (ADAS) aimed at improving road safety. These new uses require more highly detailed and precise maps. The use of a datalog vehicle to collect roadway geometry data can fulfil these specifications. This paper presents fast, accurate measurement with an on-board inertial system together with a method to evaluate measurement uncertainty, particularly for any variables obtained indirectly. It also presents an algorithm for segmentation and fitting geometric curves to the experimental points, following current highway design standards. The algorithms have been applied to real road measurements. Segmentation has been done in straight alignments, circular curves and transition curves whose characteristic parameters are calculated. It has been seen that with a very small data set it is possible to reconstruct the measured geometry with few discrepancies regarding the experimental points.     

Association of reduced visibility with crash outcomes

Most of the information necessary for driving a vehicle is regarded as visual information. In spite of its importance, visibility conditions at the time of a crash are often not documented at a high level of detail. Past studies have not examined the quantified values of visibility and its association with crashes. The current study merged data collected from the National Oceanic and Atmospheric Administration (NOAA) with 2010–2012 Florida crash data. From the thousands of logged weather events compiled by the NOAA, the researchers isolated periods of normal visibility and comparable periods of reduced visibility in a matched-pairs study. The NOAA data provided real visibility score based on the spatiotemporal data of the crashes. Additionally, the crash data, obtained from Roadway Information Database (RID), contains several geometric and traffic variables that allow for effects of factors and visibility. The study aims to associate crash occurrence under different levels of visibility with factors included in the crash database by developing ordinal logistic regression. The intent is to observe how different visibility conditions contribute to a crash occurrence. The findings indicate that the likelihood of a crash increase during periods of low visibility, despite the tendency for less traffic and for lower speeds to prevail during these times. The findings of this study will add valuable knowledge to the realm of the impact of visibility in the way of using and designing appropriate countermeasures.     

Estimating bus run times for new limited-stop service using archived AVL and APC data

In recent years, several transit agencies have been trying to be more competitive with the automobile to attract choice riders. Transit agencies can only be competitive if they can provide services that are reliable, have a short access and egress time, and have run times that are comparable to automobiles. Several transit agencies try to be competitive through offering faster service, such as limited-stop (express) bus service. This study uses AVL and APC data, in addition to a disaggregate data obtained from a travel behavior survey, to select stops and estimate run times for a new limited-stop service that will run parallel to a heavily used bus route (67 Saint-Michel) in Montréal, Canada. Three different scenarios are developed based on theory and practice to select stops to be incorporated in the new limited service. The time savings for each scenario are then evaluated as a range and a fourth scenario is developed. A limited-stop service is recommended based on selecting stops serving both directions of the route, major activity points and stop spacing. This study shows that implementing a limited-stop service would yield substantial time savings for both, the new limited service and the existing regular service running in parallel.     

Computing 4D near-optimal trajectories for dynamic air traffic flow management with column generation and branch-and-price

Abstract

The current air traffic system faces recurrent saturation problems. Numerous studies are dedicated to this issue, including the present research on a new dynamic regulation filter holding frequent trajectory optimisations in a real-time sliding horizon loop process. We consider a trajectory optimisation problem arising in this context, where a feasible four-dimensional (4D) trajectory is to be built and assigned to each regulated flight to suppress sector overloads while minimising the cost of the chosen policy. We model this problem with a mixed integer linear programme and solve it with a branch-and-price approach. The pricing sub-problem looks for feasible trajectories in a dynamic three-dimensional (3D) network and is solved with a specific algorithm based on shortest path labelling algorithms and on dynamic programming. Each algorithm is tested on real-world data corresponding to a complete traffic day in the European air traffic system; experimental results, including computing times measurement, validate the solution process.     

Modeling isoexposure to transit users for market potential analysis

Transit operators face a difficult fiscal environment and an imperative to contribute to urban sustainability. Under these circumstances, operators must find innovative ways to make public transportation attractive to broader segments of the public, while simultaneously trying to raise revenue to reduce reliance on public subsidies. Development of commercial partnerships is seen as a promising way to achieve these goals. Previous research has examined the potential of using geodemographics to assist transit agencies in the task of identifying potential partners for developing mutually beneficial commercial agreements. In this paper we describe an approach to model isoexposure to transit users as a tool to assess market potential. The approach is based on the analysis of walking behavior of transit users, and specifically distance walked at the end of their transit trip. Spatial modeling is used to geographically project estimates of walking distance for a desired demographic profile at a specific transit facility. After expanding the estimates using sample weights, overlays of these estimates can be used to generate variations in exposure to transit travelers at different locations in space. The approach is demonstrated using the case of Metro users in Montreal, Canada. The case study demonstrates the use of isoexposure profiles as a novel approach to generate marketing intelligence. This should be of interest to transit agencies and businesses interested in developing partnerships.     

Multicriteria Evaluation of High-speed Rail,Transrapid Maglev and Air Passenger Transport in Europe

This article deals with a multicriteria evaluation of High-Speed Rail, Transrapid Maglev and Air Passenger Transport in Europe. Operational, socio-economic and environmental performance indicators of the specific high-speed transport systems are adopted as the evaluation criteria. By using the entropy method, weights are assigned to particular criteria in order to indicate their relative importance in decision-making. The TOPSIS method is applied to carry out the multicriteria evaluation and selection of the preferable alternative (high-speed system) under given circumstances.     

Parametric roll mitigation using rudder control

Severe roll angles can be developed by parametric excitation in relatively moderate weather without any apparent pre-warning for the crew onboard. In this study the prospect of using rudder control to mitigate parametric roll was investigated using multi-degree of freedom simulations. A typical modern Pure Car and Truck Carrier was considered and modelled by coupling a roll model with a planar motion manoeuvring model. The combined model was calibrated using in-service, full-scale trials and model tests. Irregular variations of the metacentric height were applied to simulate recorded, full-scale events of parametric roll that have occurred with the considered design. These simulations with rudder roll control showed promising results and demonstrate that the approach could be very efficient for mitigation of parametric roll.     

Planning vessel air emission regulations compliance under uncertainty

In this paper we consider the reduction of air emissions from vessels when uncertainty is taken into account. Uncertainty in the reduction effects of the different existing air emission controls is currently high and makes their selection for vessel emission regulations compliance a challenging process. We develop a two-stage stochastic optimization model that addresses this uncertainty. The model’s objective is to plan the installation of air emission controls over a specified time horizon for a vessel to comply in the most cost-efficient way with the air emission regulations. The uncertain reduction effects of the controls are modelled by a set of scenarios. The approach is applied to a case study with real data. The solution exposes the important impact of uncertainty on this problem, especially on the SO _X reduction, while the CO₂ reduction plan seems in this case not affected by uncertainty.     

Optimized selection of vessel air emission controls—moving beyond cost-efficiency

Shipping currently has an unexploited potential for improved energy efficiency and reduced emissions to air. Many existing air emission controls have been proved to be cost-efficient but are still not commonly installed on board vessels. This paper discusses the so-called ‘energy paradox’ in maritime transportation, presenting barriers to overcome and criteria to consider when selecting cost-efficient air emission controls. Current approaches typically select available controls based on their cost-effectiveness. While this is an important aid in the decision-making process, and, in relative terms, easy to quantify, it is not a sufficient criterion to capture the true preferences of the decision-maker. We present in this paper a multi-criteria optimization model for the selection of air emission controls. This decision framework can also incorporate subjective and qualitative factors, and is applied to the shipping company Grieg Shipping. A survey among internal Grieg Shipping stakeholders identifies the important criteria to consider, their relative importance, and the scoring of the controls. This empirical data is used as parameters in the model and the model is then applied on a vessel of the Grieg Shipping fleet. The results show that nonfinancial factors play an important role in the selection of air emission controls in shipping.     

L’exploitation des données de cartes à puce à des fins de planification des transports collectifs urbains

Smart card fare collection systems are widely used nowadays in urban public transport networks. These systems are bound to facilitate the collection and management of revenues in transit authorities. However, since smart card systems collect a large amount of data on a daily basis, they can be exploited to better characterize the demand and supply of public transport in subways, tramways and bus networks, while data at an individual level should remain strictly confidential. The spatial and temporal dimensions of the data make it very interesting for planning purposes, but the data must first be validated and completed before further analysis. This article presents the results of five years of research conducted in collaboration with theSociété de transport de l??Outaouais, in Quebec. The following analyses are presented: error processing, estimation of alighting points, diffusion of operational statistics, analysis of user behaviour, analysis of network performance, comparison with household survey data and user loyalty modelling.