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Introduction: Centralization of trauma patients has become the standard of care. Unfortunately, overtriage can overcome the capability of Trauma Centres. This study aims to analyse the association of different mechanisms of injury with severe or major trauma defined as Injury Severity Score (ISS) greater than 15 and an estimation of overtriage upon our Trauma Centre. Methods: A retrospective review of our prospective database was undertaken from March 2014 to August 2016. Univariate and multivariable logistic regression models were used to estimate the association between covariates (gender, age, and mechanisms of injury) and the risk of major trauma. Results: The trauma team (TT) treated 1575 patients: among the 1359 (86%) were triaged only because of dynamics or mechanism of trauma. Overtriage according to an ISS < 15, was 74.6% on all trauma team activation (TTA) and 83.2% among the TTA prompted by the mechanism of injury. Patients aged 56–70years had an 87% higher risk of having a major trauma than younger patients (OR 1.87, 95% CI 1.29–2.71) while for patients aged more than 71years OR was 3.45, 95% CI 2.31–5.15. Car head-on collision (OR 2.50, 95% CI 1.27–4.92), intentional falls (OR 5.61, 95% CI 2.43–12.97), motorbike crash (OR 1.67, 95% CI 1.06–2.65) and pedestrian impact (OR 2.68, 95% CI 1.51–4.74) were significantly associated with a higher risk of major trauma in a multivariate analysis. Conclusions: Significant association with major trauma was demonstrated in the multivariate analysis of different mechanisms of trauma in patients triaged only for dynamics. A revision of our field triage protocol with a prospective validation is needed to improve overtriage that is above the suggested limits.

The paper outlines an innovative and efficient pedestrian protection system. It is mobile phone based and easy to implement. It detects and evaluates context and surroundings of vulnerable road users to reduce their number of accidents, injuries and fatalities. Involved stakeholders and potential business scenarios are identified.

In several biological systems, crowding can occur (ants, bees, birds, fishes, people, etc.). In human societies, crowding affects several aspects of our lives. Human crowds are considered to be one of the most complex systems. Their dynamics, resulting from individual interactions and giving rise to fascinating phenomena, is often difficult to understand and hard to model, which intrigues experts from different fields. The technological advancement, especially in computer performance, has allowed to model and reproduce pedestrian movement more realistically. Nowadays, researchers from different disciplines, such as social sciences and bio-mechanics, are better equipped to examine and understand the crowd movement and pedestrian interactions. Professionals from architects and transport planners to fire engineers and security advisors are also interested in crowd models that allows them to optimize the design and operation of a facility. In the phase of modelling pedestrian and crowd movement, the role of mechanics is prime. Using the principles of mechanics, the necessary equations of new models are derived so that scientists are able to simulate pedestrians’ cognitive and behavioral mechanisms as observed by psychologists and sociologists. Finally, it is worth mentioning that by only using the equations from collision theory, several collective phenomena were well reproduced.

In order to enhance the midblock crossing safety of pedestrians at special locations, such as entrance of schools, hospitals, and supermarkets, the actuated signal control system is proposed to provide the special signal for vulnerable pedestrians’ crossing behavior, especially for the peak hours. Pressure sensors and inductive loops are applied to detect the number of pedestrians and the volume of approaching vehicles, and system function principle is designed to improve the efficiency and accuracy of signal control with a signal timing model. A roadway segment near Cuihua Road School is chosen as an example and the corresponding signal timing model is calibrated according to the geometrics of Cuihua Road. The layouts of detectors and facilities are allocated to protect pedestrians’ crossing at midblock crossings. The analysis result shows the efficiency of this method.

Streetscapes satisfy various human needs in terms of contact with nature, aesthetic preference, and recreation (Matsuoka and Kaplan in Landsc Urban Plan 84:7–19, 2008). They provide a place for people (consciously or unconsciously) to interact with a place by either observation or participation. Streetscapes are also key factors in promoting outdoor activities, whose benefits help maintain the vitality of a place. In particular, streetscapes have significant influences on pedestrian movements (Foltệte and Piombini in Landsc Urban Plan 81:225–234, 2007). Historical streetscapes are sites where urban daily life is presented, and where the memory of human culture is manipulated and embedded within the ordinary landscape. As such, historical streets play a crucial role in preserving locality in cities (Drozdzewski in Polit Geogr 42:66–78, 2014).

The widespread deployment of Wi-Fi communication makes it easy to find Wi-Fi access points in the indoor environment, which enables us to use them for Wi-Fi fingerprint positioning. Although much research is devoted to this topic in the literature, the practical implementation of Wi-Fi based localization is hampered by the variations of the received signal strength (RSS) due to e.g. impediments in the channel, decreasing the positioning accuracy. In order to improve this accuracy, we integrate Pedestrian Dead Reckoning (PDR) with Wi-Fi fingerprinting: the movement distance and walking direction, obtained with the PDR algorithm, are combined with the K-Weighted Nearest Node (KWNN) algorithm to assist in selecting reference points (RPs) closer to the actual position. To illustrate and evaluate our algorithm, we collected the RSS values from 8 Wi-Fi access points inside a building to create a fingerprint database. Simulation results showed that, compared to the conventional KWNN algorithm, the positioning algorithm is improved with 17 %, corresponding to an average positioning error of 1.58 m for the proposed algorithm, while an accuracy of 1.91 m was obtained with the KWNN algorithm. The advantage of the proposed algorithm is that not only the existing Wi-Fi infrastructure and fingerprint database can be used without modification, but also that a standard mobile phone is sufficient to implement our algorithm.

The research of pedestrian tracking algorithm in infrared image sequence is a curial part in video surveillance. But due to the special characteristics of infrared image, such as low contrast, fuzzy edge and unknown noises interference, the study of infrared pedestrian tracking algorithm becomes a great challenge. Spatio-temporal slice method is an effective analysis method considering both space and time. In view of the existing tracking methods based on spatio-temporal slice only considering horizontal slice analysis and usually with large amount of calculation, this paper proposes a novel tracking algorithm based on spatio-temporal slice trajectory analysis. The proposed algorithm uses multi-layer horizontal and vertical slices to obtain the complete trajectories in order to determine the information of target boundary and position, and then tracks the target in each frame of the infrared image sequence. The experimental results show that the algorithm has relatively high tracking accuracy at a fast computing speed. Moreover, it performs effectively in different infrared image sequences with various motion modes by single pedestrian from OTCBVS/05 Terravic Motion IR Database, namely, the algorithm has good robustness to some extent.

Numbers of studies have shown that electroencephalogram (EEG) signals can objectively reflect the physiological and mental state of a human being. In order to explore the influence of traffic conditions on the indirect vision drivers’ EEG, we asked the volunteer drivers to drive a car equipped with indirect vision driving system around the test loop for 3 times and collected the drivers’ EEG signals in real time. Meanwhile, a driving video recorder equipped on the windshield was used to record the traffic conditions of every lap. Contrasting the videos of traffic conditions, the changes of EEGs were analyzed. The result shows that the average attentions of indirect vision the driver driving on the curve lanes and when the vehicle starts to move or stops to run are higher than driving on the straight section, and the transient attention of the indirect vision driver appears peaks when meeting another vehicle, bike, and pedestrian. It means that complex traffic conditions and operant tasks could make indirect vision driver’s psychology burden heavier.

Most of the phone has been configured IMU currently, includes an acceleration sensor and an electronic compass. In this paper, using the pedestrian dead reckoning algorithm based accelerometer and electronic compass composition IMU to assist indoor positioning. Detection walking step length and state. Currently detect direction of motion requires the user wear or hand in IMU with a fixed posture. To address this issue, using the terminal gesture recognition algorithm in the third quarter. When pedestrian walking on the same direction, the angle walking forward and walking backward was the same. To address this issue, using pedestrian movement direction detection method based on the differential cross-correlation of the acceleration in the fourth quarter. Based on the above, the article conducted experiments in Section V and the result shows that the proposed method can effectively detect pedestrian movement forward and backward state, the average accuracy of the detection results is 85.67 %.

PDR (Pedestrian Dead Reckoning) is a relative positioning method using step length and heading. However, the step length is varying while a pedestrian performs different motions. With estimated motion state, the adaptive step length model can be applied. Considering that human motion is a continuous procedure, in this paper, we propose a sequence-based motion recognition method which estimates the motion states from a sequence of data. In contrast with traditional classifiers, this paper deploys a HMM (Hidden Markov Model) to infer the state labels of sequence motion. And with the assist of motion recognition, a 3D indoor pedestrian localization is presented. Experimental results show that the classification accuracy of sequence-based motion recognition is improved comparing to that of using Naive Bayes classifier on standalone motion states. Furthermore, the positioning accuracy of a pedestrian in indoor environments is promoted using proposed method in this paper. The mean positioning error is reduced from 0.78 to 0.30 m. The 50th and 95th percentile errors are also cut down in the test within a typical office building.

During the past decade the implementation of energy harvesting sensor technology, at micro scale, has occurred due to the rapid growth of low-powered device usage, such as mobile phones, laptops, and the development of LED lights significantly increasing in efficiency. Studies have demonstrated that the ability of this technology to harvest energy from the human body, such as footfalls, can be used in the generation of electricity. Piezoelectric sensor technology has been investigated for this purpose, due to its significant advancement in the efficiency and its application in a variety of designs. This research investigates how pedestrians can become generators of their own service, through the use of piezoelectric sensor technology, in the form of safety lighting. Proposed urban design scenarios explore the opportunity implementing a piezo power-generating device along high traffic pedestrians pathways in the City of Melbourne (Australia), evaluating real time and storage options, considering harvesting the energy during the day and using it at night time when needed.

Based on the purpose of pedestrian protection, a new Pop-up Engine Hood System (PEHS) with an effect of elastic butter is developed to reduce the injury of pedestrians in unavoidable human–car collision situation. Using the fusion of computer vision and laser scanner technology is the best way to tackle the problem of both responsiveness and robustness. Critical parameter of the device is determined. And a reusable PEHS using mechanical spring is designed by using optimization method. After setting up the 3-dimensional model and physical model, the performance of bouncing and injury protection of the system has been proved by kinematics simulation and prototype experiment.

In the virtual environment, the current methods can simulate some virtual human behaviors realistically. However, these methods take the crowd as a collection of separate individuals, which is difficult to generate interactive behaviors of the virtual crowd, especially for the small pedestrian groups with fewer members. Based on establishing the basic set of formations, this paper will model the walking behavior of small pedestrian groups, and control the interaction between group members. Regarding to the group with more members, we firstly separate the group into multiple smaller groups, and design an algorithm to maintain the consistency and adhesion between these detached groups. The simulation results show that the small pedestrian groups generated by our method can adjust their formations automatically according to the changing environment, and accomplish collision-free movement. Visually speaking, the behaviors of the small group we model are authentic.

In this paper, we propose a method to summarize an egocentric moving video (a video recorded by a moving wearable camera) for generating a walking route guidance video. To summarize an egocentric video, we analyze it by applying pedestrian crosswalk detection as well as ego-motion classification, and estimate an importance score of each section of the given video. Based on the estimated importance scores, we dynamically control video playing speed instead of generating a summarized video file in advance. In the experiments, we prepared an egocentric moving video dataset including more than one-hour-long videos totally, and evaluated crosswalk detection and ego-motion classification methods. Evaluation of the whole system by user study has been proved that the proposed method is much better than a simple baseline summarization method without video analysis.

The aim of the study is twofold: firstly to compare the possibilities of using GPGPU (General-Purpose Computing on Graphics Processing Units) in continuous and discrete crowd dynamics simulation, secondly to draw conclusions on the applicability of GPUs in engines of professional crowd simulations. For this purpose the authors have implemented two models of pedestrian dynamics: continuous - Social Forces model and discrete, Cellular Automata based - Social Distances model. The presented simulations refer to outdoor, large area pedestrian movement.

A cellular model of pedestrian dynamics based on the Floor Field model is presented. Contrary to the parallel update in Floor Field, the concept of adaptive time span is introduced. This concept, together with the concept of bounds, supports the spontaneous line formation and chaotic queue in front of the bottleneck. Model simulations are compared to the experiment “passing through”, from which a phase transition from low to high density is observed.

Pedestrian Navigation System (PNS) is useful for emergency responders, security personnel, and wide range augmented reality applications. Since the majority of these pedestrian navigation applications require walking from outdoor to an indoor destination point, the Inertial navigation System (INS) which is self-contained and immune to jamming/interference are preferred to be used. Due to the sensor properties, a navigation solution of a stand-alone inertial system will drift rapidly, so it relies on external corrections to maintain the system stability and reliability, such as Global Positioning System (GPS). In addition to seeking for external compensation, taking use of constraints deduced from the particular properties of pedestrian application is also well presented. Compared with using external augmentation, these constraints are advantaged of keeping the system independence. In this paper, the walking status detection based on inertial sensor’s properties is investigated. Two constraints are considered to reduce the divergence of the navigation solution caused by the signal errors of accelerometer and gyroscope. One is position update by step detection and the other is the heading angle constraint based on walking status detection. Experiments results show that different walking status: static, walking forward, or turning, can be well distinguished by the signal properties during walking.

Pedestrian violation has become one of the social focus problems. By classifying pedestrian violations into different kinds and considering external perception as an important variable, this study applied a revised Theory of Planned Behavior (TPB) to develop a questionnaire to explore the psychological mechanism of pedestrian violations. The results show that the revised TPB model fits well and can explain the pedestrian violations.

In this work we aim to develop an onboard monocular pedestrian detection system by employing a classifier based on multi-level features. Different from most state-of-the-art detectors, our system employs a set of multi-level features to ensure high detection rate. More specifically, a set of EOH and OLBP based multi-level features is used to describe cell-level and block-level structure information. Multi-level features capture larger scale structure information which is more informative for pedestrian localization. Experiments on the INRIA dataset and the Caltech pedestrian detection benchmark demonstrate that the new pedestrian detection system is not only comparable to the existing pedestrian detectors but it also performs at a faster speed.

To sustainably introduce electromobility in Germany research concerning the acceptance of this new form of mobility within the general public is a necessity. Thus, the Department New Mobility of the Frankfurt University of Applied Sciences has conducted two different research projects on that matter: “Elektrolowe 2010” determined the potential electric car user in Hesse, using available mobility data on traffic behaviour. The studies were conducted for three different areas which are typical for the state of Hesse: a monocentric (Kassel), a polycentric (Frankfurt), and a rural region (Lauterbach). One main result was that less than 10 % of all respondents in all areas stated that they travel 80 km or less per day. Considering that an average electric car has a range of at least 100 km before the battery needs to be recharged, the often discussed range issue is not a problem for most people on an average day. From this point of view, there is a great potential for electric vehicles in Hesse. Within the Model Region Electromobility Rhine-Main social-scientific accompanying research has been conducted. Through quantitative surveys it was possible to gain detailed information about the users’ acceptance and the mobility behaviour before and after the introduction of electric vehicles.Survey results show that, in general, users were highly motivated. Even though the respondents have a similar background and form a rather homogenous group as far as age and gender are concerned, it is still possible to draw overall conclusions: electric cars are especially suitable for thedaily commute to work. Only 2 % of all users within the survey have journeys of more than 100 km to get to work, and could easily use an electric car for their daily commute. Electric vehicles do not only replace regular cars but also bikes and pedestrian traffic. Public transport is not affected by electric vehicles, especially not for commuting. For short distances of 10 km or less Pedelecs are an alternative to non electric motorized vehicles. Even though most respondents have an overall positive attitude towards electromobility, only a few would consider buying an electric car due to the high costs of purchase. However, certain incentives, such as tax reduction and free parking, would change peoples’ minds.

Despite being widely adopted and rigorously followed in many successful pedestrian detectors, the original HOG (Histogram of Oriented Gradients) descriptor are in fact NOT optimally tuned for pedestrian detection. To address this issue, we quantitatively investigate the interplay among different HOG parameters, in particular that among the cell size, aspect ratio, and detection window size, which makes it possible to jointly tune these parameters to achieve better pedestrian detection performance. In addition, we extend the training procedure of the original HOG-based detector of Dalal et al. through presenting an automatic positive sample generation algorithm, introducing LSVM (Latent SVM) to iteratively optimize the positive training samples, and adopting a hard negative mining method. To verify the effectiveness of our improved detector, we conduct extensive experiments on INRIA Person, TUD-Brussels and Caltech Pedestrians datasets. On all these datasets, our detector outperforms significantly the original HOG detector of Dalal et al.

Tracking of pedestrians from a moving vehicle equipped with a monocular camera is still considered as a challenging problem in the fields of both computer vision and robotics. In this paper, we address this problem in a particle filter framework, which well incorporates different cues from detector, dynamic model and target-specific tracking. In order to eliminate the effect of ego-motion when predicting the movement of pedestrians, we train one dynamic model for each driving behavior (moving forward, turning left/right) given a set of training trajectories. The learnt dynamic model is then utilized to predict the future movement of the pedestrian in the tracking process. We demonstrate our system works robustly on challenging dataset with strong illumination changes.

If a vehicle has to drive autonomously in an area with mostly pedestrian traffic and thus needs to share the free space with pedestrians it has to safely navigate in this environment. A pedestrian area lacks fixed lanes and definite traffic rules which leads to more complex paths. Classical path planning in a partially known environment with multiple moving pedestrians is a hard planning problem. Just the task of accurately predicting the individual path of multiple pedestrians in a group accounting for all interactions between the different persons over more than a few seconds is as good as impossible. Using this faulty prediction as a foundation for a path planning algorithm will produce a flawed plan. However such an environment is easy for humans or animals to navigate, avoidance of other persons and moving in a group of pedestrians happens almost unconsciously. In this paper a simple way of autonomously navigating such an environment by using a pure reactive control based on a few simple rules is explored. This method doesn’t need a completely known environment, doesn’t plan ahead, is simple to implement and can react quick to changes in the environment.

The improvement of safety at mass events has become an important issue not only due to several disasters involving large crowds. To support security services in case of emergencies we have developed an evacuation assistant which allows forecasting the emergency egress of large crowds in complex buildings. Such a forecast requires pedestrian models which produce realistic crowd dynamics and, at the same time, can be simulated faster than real-time. Here we give an overview of the project and present first results.

Since its introduction in the Workshop on Grand Challenges for Modeling and Simulation in Dagstuhl 2002, Symbiotic Simulation has proven its versatility in many diverse areas ranging from manufacturing to pedestrian evacuation. This paper presents a new application of Symbiotic Simulation in the simulation and generation of traffic incident management strategies. The framework for the generation and evaluation of incident management strategies is used in which Symbiotic Simulation is the core technique of the Strategy Generation Module. Preliminary experimental result shows the effectiveness of Symbiotic Simulation in helping to simulate and select the best strategy to improve the traffic condition. Challenges are also highlighted with potential research direction.

Methods of automatic recognition of traffic and pedestrian control means are in high demand today. Many problems in this field are not solved yet, or solved with significant restrictions in their work. Our approaches and methods help to advance these complex problems closer to practically important goals. Here we describe our approaches to smartphone-based recognition of traffic lights, traffic signs and “zebra” pedestrian crossings. These are all needed and very important for ensuring road safety and traffic regulation.

In this paper, a hybrid indoor location tracking method is proposed for pedestrian using a set of inertial sensors embedded in smartphones. The method is composed of two localization techniques; one is dead-reckoning using inertial sensors and the other is Wi-Fi fingerprinting. The proposed method uses the concept of combined map of topological and geometric map. Introducing user-select points of interest in his/her workplace we can reduce the cost of building a radio map for Wi-Fi fingerprinting method. The dead-reckoning method can track incremental movements of user by detecting steps. Based on acceleration signals we proposed a method to estimate the orientation and position of the phone in a pocket of pants. Experiments verified the performance of the method.

We present a novel approach for pedestrian detecting on moving vehicle which equipped with low-cost cameras. Our approach is working in a framework which combines two-dimensional human body characteristics and three-dimensional information such as parallax and distance. By constructing a SPM (surface parallax map), it calculates parallax of object which do not belong to the road plane such as human body and obstacles. After recording the scores of all road area, an occlusion image is created, in which high density area indicates people's most likely appearance. Then a SVM (support vector machine) classifier is trained to classify pedestrian and non-pedestrian windows in candidate area. We also propose an algorithm to maintain SPM in real time. We evaluate our approach on real data which are taken from crowded city areas, the efficient and accurate results are demonstrated.