卧铺车厢旅客信息管理系统设计

为了改善我国铁路卧铺列车当前落后的旅客信息管理现状,设计了一种基于嵌入式的旅客信息管理系统,其包括手持信息采集终端、旅客信息管理终端和到站提醒模块。系统采用二维码识别、ZigBee无线传输、CAN总线、Linux等技术,以实现卧铺旅客信息自动采集、无线传输、汇总更新、到站提醒等功能。测试结果表明,该设计能把车厢内如铺位使用情况、旅客目的到站、列车即将到达站等信息显示在旅客信息管理终端中,有效地提高了列车员的工作效率和准确率,具有较好的工程应用和推广价值。     

Development and validation of a technology acceptance model for safety-enhancing,wearable locating systems

Events of disasters on passenger ships can never be completely excluded while numbers of passengers on cruises are continuously increasing. Innovations in ubiquitous and networked computing such as wearable locating systems for passengers could enable faster muster and more efficient search for missing people. As the safety enhancement depends on the effective usage of these technologies, passengers’ acceptance is crucial, though largely unknown, and infrastructure implies high costs for shipping companies. In order to investigate passengers’ acceptance, a context-specific technology acceptance model was developed based on a literature review and qualitative interviews with passengers. The model was validated by an online survey with 2086 passengers aged between 16 and 81 years. The context-specific factors social influence, expected usefulness, trust, privacy concern, and perceived security risk explain 95% of the variance in the target variable intention to use locating systems with social influence as the strongest predictor. The context-specific factors are most determined by the passenger characteristics age and need for safety. In conclusion, the model presents insights into the relationships between passenger characteristics and context-specific factors enabling systematic interventions to increase acceptance for locating systems and, thereby, contributes to enhance safety for the occurrence of an evacuation.     

Discovering the Hidden Community Structure of Public Transportation Networks

Advances in public transit modeling and smart card technologies can reveal detailed contact patterns of passengers. A natural way to represent such contact patterns is in the form of networks. In this paper we utilize known contact patterns from a public transit assignment model in a major metropolitan city, and propose the development of two novel network structures, each of which elucidate certain aspects of passenger travel behavior. We first propose the development of a transfer network, which can reveal passenger groups that travel together on a given day. Second, we propose the development of a community network, which is derived from the transfer network, and captures the similarity of travel patterns among passengers. We then explore the application of each of these network structures to identify the most frequently used travel paths, i.e., routes and transfers, in the public transit system, and model epidemic spreading risk among passengers of a public transit network, respectively. In the latter our conclusions reinforce previous observations, that routes crossing or connecting to the city center in the morning and afternoon peak hours are the most “dangerous” during an outbreak.

     

Detection of passenger flow on and off buses based on video images and YOLO algorithm

Bus passenger flow information is very important as a reference data for bus company line optimization, schedule scheduling basis, and passenger travel mode arrangement. With the development of image processing technology, it has become a current research trend to count passenger flow with the help of surveillance video of passengers getting on and off the bus. The specific research contents of this paper based on video image detection and statistics of passengers are as follows:(1) Collect head target image samples through a variety of ways, including 3960 positive head target samples and 4150 negative head target samples, which together constitute the head target feature database. (2) Established a head target detection model based on deep learning. First, the labeling of the head target training data set is completed. Then, after 15,000 iterations of model training, the YOLOv3 head target detection network model was obtained, with a recall rate of 92.12% and an accuracy rate of 89.71%. (3) A multi-target matching tracking algorithm based on the combination of Cam-shift and YOLOv3 is proposed. First, the Cam-shift algorithm is used to track the head target. Secondly, the head target tracking data and the YOLOv3 detection data are combined to solve the problem of drift during the tracking of the Cam-shift algorithm through the data association matching method based on the minimum distance, and then combined with the time constraint, a passenger location information judgment rule is proposed. Optimize the error and missed detection in the process of head target detection and tracking, and improve the reliability of passenger trajectory tracking. (4) A statistical algorithm for the detection of passengers getting on and off the bus is proposed. First, the trajectory of passengers in the bus boarding and disembarking area is analyzed, and a process for judging passengers’ boarding and boarding behavior is proposed. At the same time, a passenger position information judgment rule is proposed according to the different situations of whether there are new passengers or missing passengers, so as to optimize the problem of wrong detection and missing detection in the process of head target detection and tracking. (5) Finally, experiments are carried out in actual bus scenes and simulation scenes. The experiment proves that the statistical algorithm for the detection of passengers getting on and off the bus proposed in this paper has good detection, tracking and statistics effects in bus scenes and simulation scenes.

     

Energy expenditure of cycle rickshaw pullers in different places in India

The cycle rickshaw is a popular transportation device. The aim of the study was to assess workload of cycle rickshaw pullers–physiological and subjective at four different places in India. Subjects were instructed to pull a cycle rickshaw with two passengers, for 20 min. Working and recovery heart rates were recorded. The mean values of energy expenditure of pulling a cycle rickshaw varied from 23.5 ± 2.66 to 25.35 ± 1.51 kJ/min. Relative cardiac strain and cardiac cost indicated that the job is ‘heavy’ to ‘very heavy’. Subjective assessment of workload was ‘heavy’ to ‘very heavy’. The combined workload assessed from physiological parameters and subjective assessment indicated that the job could be categorised as ‘heavy’ to ‘very heavy’ at all the places studied and needs to be reduced by redesigning the structural and functional components of the cycle rickshaw. The rickshaw pullers carry out the jobs many times per day, sometimes without proper rest pauses between trips. The outcome of the research project is beneficial for cycle rickshaw pullers, health administrators as well as manufacturers of cycle rickshaws. Primarily, the manufacturers would be able to use the data for producing a newer model of cycle rickshaw, which would require less energy to drive. The health administrators would be able to take policy decisions for administering better health care for the unorganised and underprivileged workers. The pullers could be guided into taking care of their health by improving their work practice, i.e. taking a sufficient rest pause between trips.     

A bus passenger re-identification dataset and a deep learning baseline using triplet embedding

Bus passenger re-identification is a special case of person re-identification, which aims to establish identity correspondence between the front door camera and the back door camera. In bus environment,it is hard to capture the full body of the passengers. So this paper proposes a bus passenger re-identification dataset,which contains 97,136 head images of 1,720 passengers obtained from hundreds of thousands of video frames with different lighting and perspectives. We also provide a evaluation applied to the dataset based on deep learning and triplet loss. After data augmentation,using ResNet with trihard loss as benchmark network and pre-training on pedestrian re-identification dataset Market-1501, we achieve mAP accuracy of 55.79% and Rank-1 accuracy of 67.91% on passenger re-identification dataset.

     

Prediction of urban human mobility using large-scale taxi traces and its applications

This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting humanmobility fromdiscovering patterns of in the number of passenger pick-ups quantity (PUQ) from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale realworld data set of 4 000 taxis’ GPS traces over one year shows a prediction error of only 5.8%. We also explore the application of the prediction approach to help drivers find their next passengers. The simulation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next passenger, by 37.1% and 6.4%, respectively.     

一种基于视距路径识别的设备无关室内定位算法

Wi-Fi技术的广泛应用和部署催生了许多基于Wi-Fi的室内定位技术。近年来,基于Wi-Fi的设备无关定位算法引起了研究人员的广泛注意。设备无关定位算法不需要目标对象携带无线传输设备,而是通过测量目标对象对无线信号传输的影响来反向推断目标对象的位置。由于不需要目标对象携带相关设备,因此可以广泛应用于多种场合,如老人健康护理等。已有的设备无关定位技术通常需要事先采集训练数据,因此容易受室内复杂多变的环境干扰,导致定位精度下降。 提出一种基于视距路径检测的设备无关定位算法。利用物理层信道状态信息CSI,可以判断一对无线收发设备之间的路径是否是视距LoS路径。在此基础上,提出一个新的设备无关定位算法,该算法在监测区域部署一组Wi Fi收发装置,对任意一对无线设备,通过识别它们之间是否存在视距路径来判断目标对象是否在这对设备的菲涅耳区域内。此外,还提出一种基于投票的方法来获得目标对象的最可能位置。在实际设备上的实验结果表明,该定位算法可以达到0.5 m左右的精度,并且不需事先训练,具有较高的实时性。     

面向乘客策略行为的航空公司舱位控制与动态定价模型

将动态控制舱位开放作为策略,研究乘客具有策略行为时航空公司舱位控制与动态定价问题.通过建立以舱位开放和价格变化为决策变量的多周期动态规划模型,讨论开放舱位和最优定价应满足的条件,并通过比较得出:实行动态舱位控制策略可缓解乘客策略行为对航空公司期望收益的影响.最后应用算例分析乘客策略程度对航空公司价格和期望收益的影响,同时发现:舱位控制不能完全消除乘客策略行为对期望收益的影响,但供应水平越高或乘客策略程度越大,其缓解策略行为的效果越明显.     

Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims to determine passenger demands (PDs) and to evaluate passenger satisfaction by using a combination of online review analysis and large-scale group decision-making (LSGDM). By using web crawler technology, online reviews related to HSR were harvested from a microblogging platform to extract PD data and information. The six PDs that reflect the most frequent concerns of passengers were identified by analyzing the online reviews. The level of satisfaction of passengers with respect to these PDs was analyzed based on the online responses from 100 HSR passengers and by adopting the interval-valued two-tuple linguistic representation model. The final degrees of satisfaction and rankings of the PDs were then determined by using the LSGDM approach with the k-means clustering method and a consensus-reaching process. This research thus constructs an index system of HSR passenger satisfaction evaluation based on online-review analysis and evaluates the process by using LSGDM approaches. The conclusions provide insights into the improvements desired by HSR passengers for in-cabin services and to improve passenger satisfaction.     

Efficient location aware intrusion detection to protect mobile devices

This paper addresses the problem of efficient intrusion detection for mobile devices via correlating the user’s location and time data. We developed two statistical profiling approaches for modeling the normal spatio–temporal behavior of the users: one based on an empirical cumulative probability measure and the other based on the Markov properties of trajectories. An anomaly is detected when the probability of a particular (location, time) evolution matching the normal behavior of a given user becomes lower than a certain threshold, determined by controlling the recall rate of the model of the normal user’s behavior. We used compression techniques to reduce processing overhead while maintaining high accuracy. Our evaluation based on the Reality Mining and Geolife data sets shows that the proposed system is capable of detecting a potential intrusion within 15 min and with 94 % accuracy.     

Integratable magnetic shape memory micropump for high-pressure,precision microfluidic applications

Precisely controlling the flow of fluids on a microscopic scale has been a technological challenge in the field of microfluidics. Active microfluidics, where a defined manipulation of the working fluid is necessary, requires active components such as micropumps or microvalves. We report on an optimized design of an integratable, wireless micropump made from the magnetic shape memory (MSM) alloy Ni–Mn–Ga. An external magnetic field generates a shape change in the MSM material, which drives the fluid in a similar fashion as a peristaltic pump. Thus, the pump does not need electrical contacts and avoids the mechanical parts found in traditional pumping technologies, decreasing the complexity of the micropump. With a discrete pumping resolution of 50–150 nL per pumping cycle, which is further scalable, and a pumping pressure well exceeding 2 bar, the MSM micropump is capable of accurately delivering the fluids needed for microfluidic devices. The MSM micropump is self-priming, pumping both liquid and gas, and demonstrates repeatable performance across a range of pumping frequencies. Furthermore, it operates simultaneously as both a valve and reversible micropump, offering superior possibilities compared to existing technologies within the flow rate range of 0–2000 µL/min. Due to its simplicity, this technology can be scaled down easily, which lends itself for future integration into lab-on-a-chips and microreactors for life science and chemistry applications.     

An airport passenger terminal simulator: A planning and design tool

Recent airport capacity studies have indicated that there is an imbalance in passenger terminal, airfield and airspace planning at many major airports. Traditionally, the emphasis has been on airfield and airspace development and analysis. Not much emphasis has been made on passenger terminal design. Therefore, there are many cases around the world exhibiting congestion problems at the airport passenger terminal as the number of air passengers continue to increase. This paper presents a generic simulation model for the final design of airport passenger terminal using SLAM II Simulation Language. The animation is presented on facility diagram which graphically portrays the layout of passenger terminal. Icons representing the entities of the system (both international and domestic passengers and baggage) are provided. The model has been verified and validated by data obtained from the Singapore Changi Airport.     

A new conceptual structure for travel information

This paper describes how to improve dynamic transport timetables. Mostly, such information as departure time, gate number, platform number, intermediate stops, and delays is arranged per flight or train. Each train or flight has one line or one column. A field observation of passengers using such a system showed that presenting information in this fashion is not optimal. Of passengers, 38% were unable to find the correct departure time. We analysed the performance of passengers. This analysis suggested that the information should not be arranged per train or flight but per destination. Each train or flight has one line or one column. An empirical comparison supported this conclusion. When a destination-based structure was used, the number of correct answers was 16% higher, the delay of each passenger was 75% less, and the time needed to search for a train decreased by 42%.     

Wireless magnetic resonant energy transfer system based on micro 3D flexible MEMS Litz coils

Wireless magnetic resonant energy transfer system shows more superiority in energy transfer efficiency and relative position parameters than that of inductive coupling system. It has been recognized as the best power supply method for implantable medical devices. The dimension and performance of the resonant coil is the key point that has great effect on the transfer efficiency and implantable applications. In this paper, high Q flexible planar and roll Litz coil is designed and implemented for the wireless energy transfer system. L and Q for 3D Litz coil is calculated and compared by theoretical model. The roll-Litz coil shows better performance in L and Q with smaller size comparing with the planar-Litz coil. Wireless energy transfer system based on two and four resonant coils is build up. The experimental results show that the energy transfer efficiency is improved by using the high Q 3D Litz coils. What’s more the roll-Litz four coil system shows priority in higher transfer efficiency with smaller size comparing with planar-Litz coil system. The efficiency of four-coil transfer system based on roll-Litz coils gets 48 % higher than the two-coil system with 1.0 cm transfer distance, and 10 % higher than the four-coil transfer system based on planar-Litz coils with 1.5 cm transfer distance.     

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Bus passenger flow calculation system is a critical part of the smart public transportation framework. Bus passenger flow information can help to make data statistics report of the passenger at a bus station which can be used by public transport operator to evaluate the quality of the transportation. Statistics report of crowded passengers in the bus station help managers to understand the bus transit operations, can provide the database for the intelligent transportation scheduling, help to provide more and better services for passengers, overall data statistics of passengers has important practical significance to improve public transport environment. This paper presents a passenger counting algorithm based on hybrid machine learning approach. In the first step, an advanced method is used to extract the Histogram of oriented gradients (HOG) feature of passenger’s heads. Classification of head features is done by using support vector machine (SVM) as a classifier for the liner model. Heads are detected successfully after performing all steps. In next step Kanade-Lucas-Tomasi (KLT) is used to reality head tracking, the multiple target tracking is achieved and the head motion trajectory of passenger target is captured stably. At last, the trajectory is analyzed and the automatic counting of bus passenger flow is realized. In the last step, the proposed algorithm is move to embedded system for practical implementation. In this paper, the algorithm intends to use ADSP-BF609 embedded platform for transplantation. The experimental results demonstrate that the statistical accuracy of the proposed algorithm is enhanced successfully; especially during the daytime with the good illustration, the effective counting of the passenger flow is achieved and the inward and outward passenger counting can be realized. In this paper three feature extraction models are used namely local binary patterns, histograms of oriented gradients and binarized statistical image in order to get accurate features. Furthermore, three common classification techniques including naïve bayes classifier, boosted tress and support vector machines are used for fine classification of extracted vectors obtained from different features extractors model. 94.50% accuracy is achieved when support vector machine (SVM) classifies the features extracted using Histogram of oriented gradients (HOG). SVM surpasses the accuracy obtained by Boosted tree namely 81.30% using Histogram of oriented gradients (HOG) features.

     

基于联合相似度的民航旅客不文明等级预测

为便于航空公司和机场对旅客的监管和服务决策,需要对旅客潜在不文明水平进行有效分级预测。通过社会不文明行为与民航不文明行为的相似性分析,预测旅客在民航潜在的不文明行为等级,针对两类行为描述之间存在的非同源问题,通过改进SIF(smooth inverse frequency)算法设计实现基于行为特征和处罚特征联合相似度匹配的民航潜在不文明等级预测(IPBS-SIF)算法。该算法结合不文明旅客分布规律为处罚规则制定统一量化标准,实现不文明旅客的多粒度融合度量。实验结果表明,IPBS-SIF算法在预测准确度方面相比其它算法提高了12.2%-15.1%,为民航不文明旅客的行为分析和预测提供了有效的解决方案。     

基于DBSCAN算法的城轨车站乘客聚集特征分析

发掘并掌握站内乘客群体的聚集时空变化规律,对于优化城市轨道交通线网间车辆的调度,特别是优化灾害条件下的客流组织管理等,具有积极的作用.针对具有密度分布非均匀特征的车站乘客位置数据集,提出一种基于高斯混合模型的DBSCAN聚类算法.首先,利用高斯混合模型对数据集进行密度的分层处理;然后,面向不同密度层次的数据集进行局部聚类,确定各密度层数据集的参数,并选取恰当的种子以完成局部聚类簇扩展;最后,将各密度层次数据集的聚类结果进行合并.通过标准和实测数据的计算结果表明,基于高斯混合模型优化后的DBSCAN算法,对于非均匀密度分布的乘客位置分布数据具有更好的聚类效果.     

Filling Space in Public Transport by Passengers

Modeling of motional behavior of the passengers in the municipal transport—bus, trolleybus, streetcar, subway, or suburban electric train—was discussed for the first time. Attention was focused on the relations for passenger flow densities that would adequately describe the main characteristics of the averaged passenger behavior and their distribution during boarding, travel, and exit. Besides studying the passenger flows and observance of schedules, these models could be used to predict the degree of bus filling, estimate the comfort level, and work out recommendations for bus configurations.     

The implementation of indoor localization based on an experimental study of RSSI using a wireless sensor network

In this paper, we present the implementation of a new indoor localization system. We studied the behavior of the Received Signal Strength Indication (RSSI) for different configurations depending on the initial energy level of the sensors used. The choice of the best XBee configuration for each sensor is obtained after studying the standard deviation of the RSSI. Thus, we performed an indoor localization application using three algorithms based on the RSSI fingerprinting. Several experiments were conducted on an established test bed made of a certain number of XBee wireless sensors. The obtained results are considered very encouraging as they are suitable to locate a person, inside a building with a precision of 80 cm and an efficiency of 90 %.