Identification of occupant posture using a Bayesian classification methodology to reduce the risk of injury in a collision

Recent studies have shown that smart restraint systems, which will recognize and then adapt to a specific collision and occupant combination, have a strong opportunity to significantly reduce occupant injuries during a traffic accident. As a step toward the development of these adaptive restraint systems, this study proposes a novel methodology for the classification of pre-crash occupant posture. Various occupant postures were simulated with a human model and the corresponding data was recorded using sensor models implemented in a mid-size car interior. The sensor data was then used to train two Bayesian classifiers which categorized an unknown occupant posture as one of nine predefined classes. The posture classifiers and a look-up table which contained optimized restraint laws for each class were combined to form catalog controllers for the restraint systems. The benefit of these restraint systems with catalog controllers vs. a restraint system optimized at a nominal posture was estimated by analyzing crash simulations with the occupant in 200 different postures. While the minimum error rate classifier showed the highest correct classification rate (90%), the Bayesian minimum risk classifier estimated the highest average injury reduction (21%). As expected, the highest injury reduction (up to 45%) was recorded for the posture classes closest to the windshield, whereas the lowest injury reduction was found for the classes closest to the nominal position. While the proposed restraint system with a catalog controller requires considerable “offline” computational effort, it is more versatile in terms of using complex human models and injury criteria and is much faster during the brief decision window available than recent “online” controllers proposed previously in literature.     

In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators

Surrogate indicators are meant to be alternatives or complements of safety analyses based on accident records. These indicators are used to study critical traffic events that occur more frequently, making such incidents easier to analyse. This article provides an overview of existing surrogate indicators and specifically focuses on their merit for the analyses of vulnerable road users and the extent to which they have been validated by previous research. Each indicator is evaluated based on its ability to consider the collision risk, which can be further divided into the initial conditions of an event, the magnitude of any evasive action and the injury risk in any traffic event. The results show that various indicators and their combinations can reflect different aspects of any traffic event. However, no existing indicator seems to capture all aspects. Various studies have also focused on the validity of different indicators. However, due to the use of diverse approaches to validation, the large difference in how many locations were investigated and variations in the duration of observation at each location, it is difficult to compare and discuss the validity of the different surrogate safety indicators. Since no current indicator can properly reflect all the important aspects underlined in this article, the authors suggest that the choice of a suitable indicator in future surrogate safety studies should be made with considerations of the context-dependent suitability of the respective indicator.     

Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.     

Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.     

Urban single-lane roundabouts: A new analytical approach using multi-criteria and simultaneous multi-objective optimization of geometry design,efficiency and safety

Building safe and effective roundabouts requires optimizing traffic (operational) efficiency (TE) and traffic safety (TS) while taking into account geometric factors, traffic characteristics and local constraints. Most existing simulation-based optimization models do not simultaneously optimize all these factors. To capture the relationship among geometry, efficiency and safety, we put forward a model formulation in this paper. We present a new multi-criteria and simultaneous multi-objective optimization (MOO) model approach to optimize geometry, TE and TS of urban unsignalized single-lane roundabouts. To the best of our knowledge, this is the first model that uses the multi-criteria decision-making method known as analytic hierarchy process to evaluate and rank traffic parameters and geometric elements of urban single-lane roundabouts. The model was built based on comprehensive review of the research literature and existing roundabout simulation software, a field survey of 61 civil and traffic expert engineers in Croatia, and field studies of roundabouts in the Croatian capital city of Zagreb. We started from the basis of Kimber’s capacity model, HCM2010 serviceability model, and Maycock and Hall's accident prediction model, which we extended by adding sensitivity analysis and powerful MOO procedures of the bounded objective function method and interactive optimization. Preliminary validation of the model was achieved by identifying the optimal and most robust of three geometric alternatives (V.1-V.3) for an unsignalized single-lane roundabout in Zagreb, Croatia. The geometric parameters in variant V.1 had significantly higher values than in the existing design V.0, while approaches 1 and 3 in variant V.2 were enlarged as much as possible within allowed spatial limits and Croatian guidelines, reflecting their higher traffic demand. Sensitivity analysis indicated that variant V.2 showed the overall highest TE and TS across the entire range of traffic flow demand and pedestrian crossing flow demand at approaches. At the same time, the number of predicted traffic accidents was similar for all three variants, although it was lowest overall for V.2. The similarity in predicted accident frequency for the three variants suggests that V.2 provides the greatest safety within the predefined constraints and parameter ranges explored in our study. These preliminary results suggest that the proposed model can optimize geometry, TE and TS of urban single-lane roundabouts.     

A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation

Although various innovative traffic sensing technologies have been widely employed, incomplete sensor data is one of the most major problems to significantly degrade traffic data quality and integrity. In this study, a hybrid approach integrating the Fuzzy C-Means (FCM)-based imputation method with the Genetic Algorithm (GA) is develop for missing traffic volume data estimation based on inductance loop detector outputs. By utilizing the weekly similarity among data, the conventional vector-based data structure is firstly transformed into the matrix-based data pattern. Then, the GA is applied to optimize the membership functions and centroids in the FCM model. The experimental tests are conducted to verify the effectiveness of the proposed approach. The traffic volume data collected at different temporal scales were used as the testing dataset, and three different indicators, including root mean square error, correlation coefficient, and relative accuracy, are utilized to quantify the imputation performance compared with some conventional methods (Historical method, Double Exponential Smoothing, and Autoregressive Integrated Moving Average model). The results show the proposed approach outperforms the conventional methods under prevailing traffic conditions.     

Using automated walking gait analysis for the identification of pedestrian attributes

Collecting microscopic pedestrian behavior and characteristics data is important for optimizing the design of pedestrian facilities for safety, efficiency, and comfortability. This paper provides a framework for the automated classification of pedestrian attributes such as age and gender based on information extracted from their walking gait behavior. The framework extends earlier work on the automated analysis of gait parameters to include analysis of the gait acceleration data which can enable the quantification of the variability, rhythmic pattern and stability of pedestrian’s gait. In this framework, computer vision techniques are used for the automatic detection and tracking of pedestrians in an open environment resulting in pedestrian trajectories and the speed and acceleration dynamic profiles. A collection of gait features are then derived from those dynamic profiles and used for the classification of pedestrian attributes. The gait features include conventional gait parameters such as gait length and frequency and dynamic parameters related to gait variations and stability measures. Two different techniques are used for the classification: a supervised k-Nearest Neighbors (k-NN) algorithm and a newly developed semi-supervised spectral clustering. The classification framework is demonstrated with two case studies from Vancouver, British Columbia and Oakland, California. The results show the superiority of features sets including gait variations and stability measures over features relying only on conventional gait parameters. For gender, correct classification rates (CCR) of 80% and 94% were achieved for the Vancouver and Oakland case studies, respectively. The classification accuracy for gender was higher in the Oakland case which only considered pedestrians walking alone. Pedestrian age classification resulted in a CCR of 90% for the Oakland case study.     

A review of the road safety strategy in hong kong

Since the mid-1990s, the effectiveness of road safety measures in Hong Kong has been weakening. Six administrations in Australia, California, Great Britain (GB), Japan, New Zealand and Sweden are selected to help review the road safety activities in Hong Kong. Nine main components of the road safety strategy, including vision, objectives, targets, action plan, evaluation and monitoring, research and development, quantitative modeling, institutional framework and funding are summarized from the road safety strategies of these overseas administrations and compared to that of Hong Kong. It is found that Hong Kong's road safety activities have to be restructured to make significant improvement. In the future, a new approach structured by the nine different road safety components is recommended. The lessons learnt can be generalized to smooth the progress of other administrations at the Intermediate Stage towards the Advanced Stage of road safety development by using the short-, medium- and long-term approaches.     

On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models

The speed-density or flow-density relationship has been considered as the foundation of traffic flow theory. Existing single-regime models calibrated by the least square method (LSM) could not fit the empirical data consistently well both in light-traffic/free-flow conditions and congested/jam conditions. In this paper, first, we point out that the inaccuracy of single-regime models is not caused solely by their functional forms, but also by the sample selection bias. Second, we apply a weighted least square method (WLSM) that addresses the sample selection bias problem. The calibration results for six well-known single-regime models using the WLSM fit the empirical data reasonably well both in light-traffic/free-flow conditions and congested/jam conditions. Third, we conduct a theoretical investigation that reveals the deficiency associated with the LSM is because the expected value of speed (or a function of it) is nonlinear with regard to the density (or a function of it).     

On the imputation of missing data for road traffic forecasting: New insights and novel techniques

Vehicle flow forecasting is of crucial importance for the management of road traffic in complex urban networks, as well as a useful input for route planning algorithms. In general traffic predictive models rely on data gathered by different types of sensors placed on roads, which occasionally produce faulty readings due to several causes, such as malfunctioning hardware or transmission errors. Filling in those gaps is relevant for constructing accurate forecasting models, a task which is engaged by diverse strategies, from a simple null value imputation to complex spatio-temporal context imputation models. This work elaborates on two machine learning approaches to update missing data with no gap length restrictions: a spatial context sensing model based on the information provided by surrounding sensors, and an automated clustering analysis tool that seeks optimal pattern clusters in order to impute values. Their performance is assessed and compared to other common techniques and different missing data generation models over real data captured from the city of Madrid (Spain). The newly presented methods are found to be fairly superior when portions of missing data are large or very abundant, as occurs in most practical cases.     

A micro-level approach to measuring the accessibility of footways for wheelchair users using the Capability Model

There are a growing number of people with mobility impairments who use wheelchairs to get around the built environment. This number is likely to increase in the future due to an increasingly ageing population combined with advances in medical technology which help to overcome some of the barriers to access that have hitherto prevented people from leading as full a life as they would have liked. Footways form an integral part of the transport network and therefore it is essential they can be accessed by all people. Currently, however, there is no well-defined method to measure the accessibility of footways for wheelchair users. One aspect of a footway is the crossfall – the transverse gradient designed to facilitate surface water drainage – which adds to a wheelchair user's difficulty when progressing along the footway. This paper first reviews previous research on measuring the effect of crossfalls on wheelchair accessibility, highlighting the need for a new approach. It then proposes the Capability Model as a starting point for this new approach. The model is updated and populated with an initial capability set chosen to measure footway accessibility across footways with three different crossfall gradients (0%, 2.5% and 4%). The focus is on the physical work provided by the user to the wheelchair in order to keep it travelling in a straight line. It is shown that in order to travel in a straight line when a footway is flat only a single principal capability is required: the ability to produce sufficient force over the required distance to overcome the inertia and rolling resistance and keep the wheelchair moving at the chosen velocity. When a positive crossfall gradient is introduced a second capability is required: the ability to apply different levels of force to the left and right sides of the wheelchair. It is concluded that it is possible to measure these two capabilities and these provide a good insight into the effect of crossfalls on footway accessibility for wheelchair users.     

A new approach to evaluating on-road public transport priority projects: balancing the demand for limited road-space

Despite widespread growth in on-road public transport priority schemes, road management authorities have few tools to evaluate the impacts of these schemes on all road users. This paper describes a methodology developed in Melbourne, Australia to assist the road management authority, VicRoads, evaluate trade-offs in the use of its limited road-space for new bus and tram priority projects. The approach employs traffic micro-simulation modelling to assess road-space re-allocation impacts, travel behaviour modelling to assess changes in travel patterns and a social cost benefit framework to evaluate impacts. The evaluation considers a comprehensive range of impacts including the environmental benefits of improved public transport services. Impacts on public transport reliability improvements are also considered. Although improved bus and tram reliability is a major rationale for traffic priority its use in previous evaluations is rare. The paper critiques previous approaches, describes the proposed method and explores some of the results found in its application. A major finding is that despite a more comprehensive approach to measuring the benefits of bus and tram priority, road-space reallocation is difficult to economically justify in road networks where public transport usage is low and car usage high. Strategies involving the balanced deployment of bus and tram priority measures where the allocation of time and space to PT minimises negative traffic impacts is shown to improve the overall management of road-space. A discussion of the approach is also provided including suggestions for further methodology development.

Patterns and projections of technical development in freeway surveillance and control in the U.S.A.

This paper explores the historical trends in freeway traffic management technology in the U.S., and the most likely projections for the coming two decades. First, existing computer‐supervised freeway surveillance and control techniques are reviewed with particular emphasis on the scientific and technological landmarks which has led to the evolution of these techniques. Next, the major underlying trends which bear on the future of automated freeway surveillance and control are identified. Finally, extrapolative projections are made to determine the most likely future of this technology. The paper concludes with implications for the issues of meeting short‐term transportation needs of urban areas through more efficient use of existing transportation facilities.     

A simple algorithm for the estimation of road traffic space mean speeds from data available to most management centres

The control of the evolution of road traffic streams is highly related to productivity, safety, sustainability and, even, comfort. Although, nowadays, the findings from research efforts and the development of new technologies enable accurate traffic forecasts in almost any conditions, these calculations are usually limited by the data and the equipment available. Most traffic management centres depend on the data provided, at best, by double-loop detectors. These loops supply time means over different aggregation periods, which are indiscriminately used as the bases for subsequent estimations. Since space mean speeds are those needed in most applications (note the fundamental relationship between flow and density in traffic flow theory), most current practice begins with an error. This paper introduces a simple algorithm that the allows estimation of space mean speeds from the data provided by the loops without the need for any additional financial outlay, as long as the traffic in each time interval of aggregation is stationary and its speed distribution is log-normal. Specifically, it is focused on the calculation of the variance of the speeds with regard to the time mean, thus making possible to use the relationship between time mean speeds and space mean speeds defined by Rakha (2005). The results obtained with real data show that the algorithm behaves well if the calculation conditions help fulfil the initial hypotheses. The primary difficulties arise with transient traffic and, in this case, other specific methodologies should be used. Data fusion seems promising in this regard. Nevertheless, it cannot be denied that the improvement provided by the algorithm turns out to be highly beneficial both when used alone in the case of stationarity or as a part of a fusion.     

Green vehicle technology to enhance the performance of a European port: A simulation model with a cost-benefit approach

In this paper, we study the impact of using a new intelligent vehicle technology on the performance and total cost of a European port, in comparison with existing vehicle systems like trucks. Intelligent autonomous vehicles (IAVs) are a new type of automated guided vehicles (AGVs) with better maneuverability and a special ability to pick up/drop off containers by themselves. To identify the most economical fleet size for each type of vehicle to satisfy the port’s performance target, and also to compare their impact on the performance/cost of container terminals, we developed a discrete-event simulation model to simulate all port activities in micro-level (low-level) details. We also developed a cost model to investigate the present values of using two types of vehicle, given the identified fleet size. Results of using the different types of vehicles are then compared based on the given performance measures such as the quay crane net moves per hour and average total discharging/loading time at berth. Besides successfully identifying the optimal fleet size for each type of vehicle, simulation results reveal two findings: first, even when not utilising their ability to pick up/drop off containers, the IAVs still have similar efficacy to regular trucks thanks to their better maneuverability. Second, enabling IAVs’ ability to pick up/drop off containers significantly improves the port performance. Given the best configuration and fleet size as identified by the simulation, we use the developed cost model to estimate the total cost needed for each type of vehicle to meet the performance target. Finally, we study the performance of the case study port with advanced real-time vehicle dispatching/scheduling and container placement strategies. This study reveals that the case study port can greatly benefit from upgrading its current vehicle dispatching/scheduling strategy to a more advanced one.     

Computer vision approach for the classification of bike type (motorized versus non‐motorized) during busy traffic in the city of Shanghai

This article describes a novel approach for the binary classification of two‐wheeler road users in a dense mixed traffic intersection. The classification is a supervised procedure to differentiate between motorized and non‐motorized (human‐powered) bikes. Road users were first detected and tracked using object recognition methods. Classification features were then selected from the collected trajectories. The features include maximum speed, cadence frequency in addition to acceleration‐based parameters. Experiments were conducted on a video data set from Shanghai, China, where cyclists as well as motorcycles tend to share the main road facilities. A sensitivity analysis was performed to assess the quality of the selected features in improving the accuracy of the classification. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 93%. This research contributes to the literature of automated data collection and can benefit the applications in many transportation‐related fields such as shared space facility planning, simulation models for two‐wheelers, and behavior analysis and road safety studies. Copyright © 2015 John Wiley & Sons, Ltd.     

A fuzzy stochastic approach to the multicriteria selection of an aircraft for regional chartering

This article deals with the problem of decision support for the selection of an aircraft. This is a problem faced by an airline company that is investing in regional charter flights in Brazil. The company belongs to an economic group whose core business is logistics. The problem has eight alternatives to be evaluated under 11 different criteria, whose measurements can be exact, stochastic, or fuzzy. The technique chosen for analyzing and then finding a solution to the problem is the multicriteria decision aiding method named NAIADE (Novel Approach to Imprecise Assessment and Decision Environments). The method used allows tackling the problems by working with quantitative as well as qualitative criteria under uncertainty and imprecision. Another considerable advantage of NAIADE over other multicriteria methods relies in its characteristics of not requiring a prior definition of the weights by the decision maker. As a conclusion, it can be said that the use of NAIADE provided for consistent results to that aircraft selection problem. Copyright © 2012 John Wiley & Sons, Ltd.     

A two-phase model for dry port location with an application to the port of Dalian in China

This paper provides guidance for an optimal and reasonable dry port layout for the port of Dalian in China. We present a two-phase framework on the location of dry ports, which solves the selection of candidate inland cities and optimal dry port location choice, respectively. Fuzzy C-Means Clustering is applied to select alternative cities in the vast hinterland of the seaport of Dalian, with a view to identify evaluation factors that affect the location selection decision. A cost-minimisation linear programming solution is proposed, with the aid of a genetic algorithm, to choose the optimal location as well as capacity level among the candidate inland cities.