For next-generation smart cities, small UAVs (also known as drones) are vital to incorporate in airspace for advancing the transportation systems. This paper presents a review of recent developments in relation to the application of UAVs in three major domains of transportation, namely; road safety, traffic monitoring and highway infrastructure management. Advances in computer vision algorithms to extract key features from UAV acquired videos and images are discussed along with the discussion on improvements made in traffic flow analysis methods, risk assessment and assistance in accident investigation and damage assessments for bridges and pavements. Additionally, barriers associated with the wide-scale deployment of UAVs technology are identified and countermeasures to overcome these barriers are discussed, along with their implications.

Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges.

This paper explores the contribution of older people to society, the role of travel in this, and whether the removal of barriers to access for older people would enable them to make a greater contribution. The paper commences by considering evidence on the value of older people to society. This shows that older people contribute in terms of expenditure in shops and other services, through employment, voluntary work, childcare and taxation. Expected demographic changes mean that society will require older people to make a greater contribution in future, hence it is prudent to consider the barriers that may hinder this. A key factor is accessibility. It is shown that the travel patterns of older people reflect their contributions to society, and so barriers to travel are likely to hinder such contributions. It is shown that most older people have the physical or mental ability to travel and that most of the barriers are to do with the environment, either during the journey or at the destination. The example of providing free off-peak bus travel for older people in Britain is shown to have brought benefits for both older people and wider society. The paper is concluded by discussion about three critical issues that need to be addressed in overcoming the barriers to access for older people: access on all stages of the journey, non-tangible barriers, and cost effectiveness in removing barriers to mobility for older people. Addressing these issues should enable older people to make a larger contribution to society.

Improving Accessibility for Older People - Investing in a Valuable Asset

Rail systems have been developing rapidly in recent years aiming at satisfying the growing passenger demand and shortening passenger travel time. The line planning problem (LPP) and train timetabling problem (TTP) are two key issues at the strategic level and tactical level, laying the foundation of a high-level service quality for railway operation. In this paper, a multi-frequency LPP (MF-LPP) model and a multi-period TTP (MP-TTP) model are introduced for direct connections, with consideration of both periodic and aperiodic nature to meet strongly heterogeneous train services and reduce the capacity loss of train operating companies. A combined LPP and TTP method is designed considering timetable robustness, timetable regularity, and passenger travel time. For a given line pool, a multi-objective mixed integer linear programming model for the MF-LPP is formulated to obtain a line plan with multiple line frequencies by minimizing travel time, empty-seat-hour and the number of lines. Using the acquired line plan from the previous step, a MP-TTP model is proposed to achieve the minimal travel time, the maximal timetable robustness and the minimal number of overtakings. The two models work iteratively with designed feedback constraints to find a better plan for the rail transport system. Numerical experiments are applied to verify the performance of the proposed model and solution approach.

Combined line planning and train timetabling for strongly heterogeneous railway lines with direct connections

A road network is a critical aspect of both urban planning and route recommendation. This article proposes an efficient approach to build a fine-grained road network based on sparsely sampled private car trajectory data under complex urban environment. In order to resolve difficulties introduced by low sampling rate trajectory data, we concentrate sample points around intersections by utilizing the turning characteristics from the large-scale trajectory data to ensure the accuracy of the detection of intersections and road segments. In front of complex road networks including many complex intersections, such as the overpasses and underpasses, we first layer intersections into major and minor one, and then propose a simplified representation of intersections and corresponding computable model based on the features of roads, which can significantly improve the accuracy of detected road networks, especially for the complex intersections. In order to construct fine-grained road networks, we distinguish various types of intersections using direction information and detected turning limit. To the best of our knowledge, our road network building method is the first time to give fine-grained road networks based on low-sampling rate private car trajectory data, especially able to infer the location of complex intersections and its connections to other intersections. Last but not the least, we propose an effective parameter selection process for the Density-Based Spatial Clustering of Applications with Noise based clustering algorithm, which is used to implement the reliable intersection detection. Extensive evaluations are conducted based on a real-world trajectory dataset from 1,345 private cars in Futian district, Shenzhen city of China. The results demonstrate the effectiveness of the proposed method. The constructed road network matches close to the one from a public editing map OpenStreetMap, especially the location of the road intersections and road segments, which achieves 92.2% intersections within 20m and 91.6% road segments within 8m.

Road Network Construction with Complex Intersections Based on Sparsely Sampled Private Car Trajectory Data

Autonomous agricultural systems are a promising solution to bridge the gap between labor shortage for agriculture tasks and the continuing needs for increasing productivity in agriculture. Automated mapping and navigation system will be a cornerstone of most autonomous agricultural system. Accordingly, we propose a ground-level mapping and navigating system based on computer vision technology (Mesh Simultaneous Localization and Mapping algorithm, Mesh-SLAM) and Internet of Things (IoT), to generate a 3D farm map on both the edge side and cloud. The innovation of this system includes three layers as sub-systems that are 1) ground-level robot vehicles’ layer for conducting frames collection only with a monocular camera, 2) edge node layer for image feature data edge computing and communication, and 3) cloud layer for general management and deep computing. High efficiency and speed of mapping stage are enabled by making the robot vehicles directly stream continuous frames to their corresponding edge node. Then each edge node, that coordinate a certain range of robots, applies a new Mesh-SLAM frame by frame, whose core is reconstructing the features map by a mesh-based algorithm with scalable units and reduce the feature data size by a filtering algorithm. Additionally, the cloud-computing allows comprehensive arrangement and heavily deep computing. The system is scalable to larger-scale fields and more complex environment by taking advantage of dynamically distributing the computation power to edges. Our evaluation indicates that: 1) this Mesh-SLAM algorithm outperforms in mapping and localization precision, accuracy, and yield prediction error (resolution at centimeter); and 2) The scalability and flexibility of the IoT architecture make the system modularized, easy adding/removing new functional modules or IoT sensors. We conclude the trade-off between cost and performance widely augments the feasibility and practical implementation of this system in real farms.

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Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision

Due to factors such as snow and ice impeding drivers’ vision, the number of automobile crashes significantly rises during winter months. This study sets forth an automatic evaluation network of the risk perceived ability for motorists driving on the freeway in snow and ice environments, using a deep learning approach and the rough sets technique. First, a naturalistic driving experiment involving thirteen licensed drivers was conducted on a freeway in Jilin, China, with a crash hot spot set prior to the start of the experiment. Then multi-sensor (eye-trackers, mini-cameras, and speed detectors) apparatuses, collecting both images and numerical data, were utilized. Afterward, restricted Boltzmann machine was used to develop a deep belief network (DBN) along with training procedures. Rough sets technique was added as judgment in output layer of the DBN. Finally, fixation duration, pupil size, changes in speed, etc., were used as input impact factors and the perception conditions were used as output variables to train the network. Furthermore, after comparing the DBN-based risk perception ability network with Naive Bayes and BP-ANN (artificial neural networks with back propagations), the results indicate that the DBN-FS not only outperforms both Naive Bayes and BP-ANN, but also improves the accuracy of perceiving risky conditions. This approach can provide reference for the design of hazard detection systems of partially automated vehicles.

Sensor-based risk perception ability network design for drivers in snow and ice environmental freeway: a deep learning and rough sets approach

China is faced with a serve aging crisis, and the seniors’ long-distance travel is becoming more challenging with the rapid development of high-speed trains. This paper studies the transfer feasibility for elderly passengers passing through specific barriers at high-speed train stations. Questionnaires were distributed at Beijing High-Speed Train Station, with 716 valid samples involved. This paper divides the transfer procedure into seven phases, and proposes various points of interest at each phase with different barriers. These points of barriers are categorized into three major different types: Serious AND Common, Serious BUT NOT Common, and Common BUT NOT Serious. The quantitative relationships between each point of barriers and old travelers’ information have been explored using decision tree and binary logistics regression. As a conclusion, we suggest some recommendations for improving the transfer environment, long-distance mobility, as well as wellbeing of the older adults at high-speed train stations. To the best of our knowledge, we are the first to study the transfer feasibility at high speed train stations for seniors both qualitatively and quantitatively, especially dealing with the complicated transfer procedure.

Improving transfer feasibility for older travelers inside high-speed train station

The ability to quantitatively evaluate the visual feedback of drivers has been considered as the primary research for reducing crashes in snow and ice environments. Different colored Chevron alignment signs cause diverse visual effect. However, the effect of Chevrons on visual feedback and on the driving reaction while navigating curves in SI environments has not been adequately evaluated. The objective of this study is twofold: an effective and long-term experiment was designed and developed to test the effect of colored Chevrons on drivers’ vision and vehicle speed; a new quantitative effect evaluation model is employed to measure the effect of different colors of the Chevrons. Fixation duration and pupil size were used to describe the driver’s visual response, and Cohen’s was used to evaluate the colors’ psychological effect on drivers. The results showed the following: after choosing the proper color for Chevrons, drivers reduced the speed of the vehicle while approaching the curves. It was easier for drivers to identify the road alignment after setting the Chevrons. Cohen’s related to different colors of Chevrons have different effect sizes. The conclusions provide evident references for freeway warning products and the design of intelligent vehicles.

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A Sensor-Based Visual Effect Evaluation of Chevron Alignment Signs’ Colors on Drivers through the Curves in Snow and Ice Environment

Indoor localization and navigation have a great potential of application, especially in large indoor spaces where people tend to get lost. The indoor localization problem is the fundamental of an indoor navigation system. Existing research and commercial efforts have leveraged wireless-based approaches to locate users in indoor environments. However, the predominant wireless-based approaches, such as WiFi and Bluetooth, are still not satisfactory, either not supporting commodity devices, or being vulnerable to environmental changes. These issues make them hard to deploy and maintain. In this paper, we present Vivid, a mobile device-friendly indoor localization and navigation system that leverages visual cues as the cornerstone of localization. By leveraging the computation power at the extreme internet edges, Vivid to a large extent overcomes the difficulties brought by resource-intensive image processing tasks. We propose a grid-based algorithm that transforms the feature map into a grid, with which finding the path between two positions can be easily obtained. We also leverage deep learning techniques to assist in automatic map maintenance to adapt to the visual changes and make the system more robust. With edge computing, user privacy is preserved since the visual data is mainly processed locally and detected dynamic objects are removed immediately without saving to databases. The evaluation results show that: i) our system easily outperforms the existing solutions on COTS devices in localization accuracy, yielding decimeter-level error; ii) our choice of the system architecture is scalable and optimal among the available ones; iii) the automatic map maintenance mechanism effectively ameliorates the localization robustness of the system.

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Vivid: Augmenting Vision-Based Indoor Navigation System With Edge Computing

The invention discloses a system and a method of reserving, dispatching and managing a university school bus. The system comprises a passenger terminal, a server, a database and a school bus terminal, wherein the passenger terminal is used for sending reservation information to the server and the reservation information is stored in the database; the school bus terminal is used for acquiring real-time position information of the school bus and real-time information of a school bus seat, the information is sent to the server, and information in the database is updated in real time; the server comprises a receiving module, a dispatching module and a sending module for receiving information from the passenger terminal and the school bus terminal, dispatching the school bus and sending the generated passenger order and the school bus order to the passenger terminal and the school bus terminal respectively. The problems of difficult university school bus taking, low service level, high no-load rate and ossified dispatching can be effectively solved, and the university school bus utilization rate is effectively improved.

Liangjie Xu

Papers

Sensor-based risk perception ability network design for drivers in snow and ice environmental freeway: a deep learning and rough sets approach

Improving transfer feasibility for older travelers inside high-speed train station

A Sensor-Based Visual Effect Evaluation of Chevron Alignment Signs’ Colors on Drivers through the Curves in Snow and Ice Environment

Vivid: Augmenting Vision-Based Indoor Navigation System With Edge Computing

System and method of reserving, dispatching and managing university school bus