Maximizing the value for money of road projects through digitalization

2018 ◽  
Vol 19 (1-2) ◽  
pp. 69-92 ◽  
Author(s):  
Carlos Oliveira Cruz ◽  
Joaquim Miranda Sarmento
Keyword(s):  
Autonomous Vehicles ◽  
Technological Development ◽  
Central Element ◽  
Transportation Systems ◽  
The Road ◽  
Road Systems ◽  
Technological Developments ◽  
Road Projects ◽  
Barriers To Implementation ◽  
Using Data

Roads are a central element of transportation systems, enabling economic and social development, fostering territorial cohesion and facilitating the movement of people and cargo. Governments have devoted significant financial resources to developing and improving their road networks, and are still facing increasing pressure to ensure proper maintenance and payments to those concessionaires that developed roads under public–private partnership arrangements. As in other sectors, digitalization is paving a way towards significant changes in the way we build, operate and finance infrastructure. These changes will have a profound impact on the entire life cycle of an infrastructure, from the design and/or construction stage, to its operation and transfer. This article provides an overall overview of the main technological developments which are, or could impact road infrastructure in the short, medium and long term. For each technological development identified in our research, we analyse the potential impact on Capex, Opex and revenues as well as their level of maturity and expected lifetime for mass adoption, and also the main bottlenecks or barriers to implementation. Additionally, we explore potential savings on investment (capex) and operational costs (opex) and increase in revenues, using data from the Portuguese highway companies. Savings can represent almost 30% of capex and opex. Overall, savings and increases in revenues can represent an impact similar to 20–40% of current revenues. The findings show that digitalization and technological development in the road sector can significantly impact the economic performance of roads, thus enhancing the value of money for the society. The findings also show that there might be some excess capacity of road systems once autonomous vehicles achieve higher market penetration. However, there are still some relevant legal, regulatory, institutional and technological and economic barriers that are slowing down the digitalization process.

scholarly journals Implementation of a Sensor Big Data Processing System for Autonomous Vehicles in the C-ITS Environment

2020 ◽  
Vol 10 (21) ◽  
pp. 7858
Author(s):  
Aelee Yoo ◽  
Sooyeon Shin ◽  
Junwon Lee ◽  
Changjoo Moon
Keyword(s):  
Big Data ◽  
Autonomous Vehicles ◽  
Processing System ◽  
Intelligent Transport System ◽  
Local Dynamic ◽  
Intelligent Transport ◽  
The Road ◽  
Road Condition ◽  
Dynamic Map ◽  
Using Data

To provide a service that guarantees driver comfort and safety, a platform utilizing connected car big data is required. This study first aims to design and develop such a platform to improve the function of providing vehicle and road condition information of the previously defined central Local Dynamic Map (LDM). Our platform extends the range of connected car big data collection from OBU (On Board Unit) and CAN to camera, LiDAR, and GPS sensors. By using data of vehicles being driven, the range of roads available for analysis can be expanded, and the road condition determination method can be diversified. Herein, the system was designed and implemented based on the Hadoop ecosystem, i.e., Hadoop, Spark, and Kafka, to collect and store connected car big data. We propose a direction of the cooperative intelligent transport system (C-ITS) development by showing a plan to utilize the platform in the C-ITS environment.

scholarly journals An Integrated Model for User State Detection of Subjective Discomfort in Autonomous Vehicles

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Linear Models ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
The Road ◽  
Novel Approach ◽  
Driving Scenario ◽  
On The Road ◽  
Using Data ◽  
Subjective Discomfort

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.

scholarly journals L*a*b* color model based road lane detection in autonomous vehicles

2017 ◽  
Vol 52 (4) ◽  
pp. 273-280
Author(s):  
M Kazemi ◽  
Y Baleghi
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Color Space ◽  
Main Idea ◽  
Transportation Systems ◽  
Intelligent Vehicles ◽  
Lane Detection ◽  
The Road ◽  
Robotic Vehicle ◽  
Video Sensors

Autonomous vehicles, as a main part of Intelligent Transportation Systems (ITS), will have great impact on transportation in near future. They could navigate autonomously in specific areas or highways and city streets using maps, GPS, video sensors and so on. To navigate autonomously or follow a road, intelligent vehicles need to detect lanes. This paper presents a method for lane detection in image sequences of a camera on top of a robotic vehicle. The main idea is to find the road area using the L*a*b* color space in consecutive frames. Subsequently, by applying this model in road area and equalization of histogram and calculation of gradient image using Sobel operator, the parameters of the lane can be calculated using a Hough transform. The proposed method is tested under various illumination conditions and experimental results indicate the good performance of the proposed method.Bangladesh J. Sci. Ind. Res. 52(4), 273-280, 2017

ANALISIS PENANGGULANGAN KELONGSORAN TANAH PADA RUAS JALAN GUNUNG TUGEL PATIKRAJA BANYUMAS

2017 ◽  
Vol 14 (1) ◽  
pp. 53
Author(s):  
Arwan Apriyono ◽  
Sumiyanto Sumiyanto ◽  
Nanang Gunawan Wariyatno
Keyword(s):  
Limit Equilibrium ◽  
Retaining Wall ◽  
Limit Equilibrium Method ◽  
Pile Foundations ◽  
Soil Parameters ◽  
The Road ◽  
Realistic Option ◽  
Stable Conditions ◽  
Using Data ◽  
Wall Slope

Gunung Tugel is an area that located Patikraja Region, Southern Banyumas. Thetopography of the area is mostly mountainous with a slope that varies from flat to steep. Thiscondition makes to many areas of this region potentially landslide. In 2015, a landslideoccurred in Jalan Gunung Tugel. The Landslide occurred along 70 meters on the half of theroad and causing traffic Patikraja-Purwokerto disturbed. To repair the damage of the road andavoid further landslides, necessary to analyze slope stability. This study is to analyze landslidereinforcement that occurred at Gunung Tugel and divides into 3 step. The first step is fieldinvestigation to determine the condition of the location and dimensions of landslides. Thesecond step is to know the soil parameters and analyzes data were obtained from the field. Andthe final step is analyzed of the landslide reinforcement by using data obtained from thepreceding step. In this research, will be applied three variations of reinforcement i.e. retainingwall, pile foundation and combine both of pile foundations and retaining wall. Slope stabilityanalysis was conducted using limit equilibrium method. Based on the analysis conducted onthe three variations reinforcement, combine both of pile foundations and retaining wall morerecommended. Application of and combine both of pile foundations and retaining wall is themost realistic option in consideration of ease of implementation at the field. From thecalculations have been done, in order to achieve stable conditions need retaining wall withdimensions of 2 meters high with 2,5 meters of width. DPT is supported by two piles of eachcross-section with 0.3 meters of diameter along 10 meters with 1-meter in space. Abstrak: Gunung Tugel adalah salah satu daerah yang terletak di Kecamatan PatikrajaKabupaten Banyumas bagian selatan. Kondisi topografi daerah tersebut sebagian besar berupapegunungan dengan kemiringan yang bervariasi dari landai sampai curam. Hal inimenyebabkan banyak daerah di wilayah Gunung Tugel yang berpotensi terjadi bencana tanahlongsor. Pada tahun 2015, peristiwa longsor kembali terjadi di ruas Jalan Gunung Tugel.Kelongsoran yang terjadi sepanjang 70 meter pada separuh badan jalan tersebut menyebabkanarus lalu lintas patikraja-purwokerto menjadi terganggu. Untuk memperbaiki kerusakan jalandan mencegah kelongsoran kembali, diperlukan analisis perkuatan tanah terhadap lerengtersebut. Studi analisis penanggulangan kelongsoran jalan yang terjadi di Gunung Tugel inidilakukan dengan tiga tahapan. Tahapan pertama adalah investigasi lapangan untukmengetahui kondisi lokasi dan dimensi longsor serta mengambil sampel tanah di lapangan.Tahap kedua adalah melakukan pengujian parameter tanah dan analisis data yang diperolehdari lapangan. Tahapan yang terakhir adalah analisis penanggulangan longsor denganmenggunakan data yang diperoleh dari tahapan sebelumnya. Pada penelitan ini, akanditerapkan tiga variasi perkuatan lereng yaitu dinding penahan tanah (DPT), turap dan DPTyang dikombinasikan dengan pondasi tiang. Analisis stabilitas lereng dilakukan dengan metodekeseimbangan batas. Berdasarkan hasil analisis yang dilakukan terhadap ketiga variasiperkuatan, DPT dengan kombinasi tiang pancang lebih direkomendasikan. Penerapan DPTyang dikombinasikan dengan minipile merupakan pilihan yang paling realistis denganpertimbangan tingkat kemudahan pelaksanaan di lapangan. Dari perhitungan yang telahdilakukan, untuk mencapai kondisi stabil diperlukan DPT dengan dimensi tinggi 2 meterdengan lebar bawah 2,5 meter. DPT tersebut ditopang oleh dua tiang tiap penampangmelintang dengan diameter 0,3 meter sepanjang 10 meter dengan jarak antar tiang 1 meter.kata kunci: tanah longsor, perkuatan tanah, metode keseimbangan batas

scholarly journals CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1402
Author(s):  
Taehee Lee ◽  
Yeohwan Yoon ◽  
Chanjun Chun ◽  
Seungki Ryu
Keyword(s):  
Surface Crack ◽  
Autonomous Vehicles ◽  
Crack Detection ◽  
Model Performance ◽  
Semantic Segmentation ◽  
Road Surface ◽  
Image Brightness ◽  
The Road ◽  
Detection Model ◽  
Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

Deep learning based Blockchain solution for preserving privacy in future vehicles

2020 ◽  
pp. 1-14
Author(s):  
Varsha R ◽  
Meghna Manoj Nair ◽  
Siddharth M. Nair ◽  
Amit Kumar Tyagi
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Smart Devices ◽  
The Road ◽  
Blockchain Technology ◽  
Technological Advances ◽  
Previous Decade ◽  
Smart Things

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals ANALYSIS OF DIFFERENT VISUAL STRATEGIES OF ‘ISOLATED VEHICLE’ AND ‘DISTURBED VEHICLE’

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 853-860
Author(s):  
Nicola BONGIORNO ◽  
Gaetano BOSURGI ◽  
Orazio PELLEGRINO ◽  
Giuseppe SOLLAZZO
Keyword(s):  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Disturbed Condition ◽  
The Road ◽  
Driving Behaviour ◽  
Visual Behaviour ◽  
Road Geometry ◽  
Potential Applications ◽  
Road Characteristics

This paper analyses the driver’ visual behaviour in the different conditions of ‘isolated vehicle’ and ‘disturbed vehicle’. If the meaning of the former is clear, the latter condition considers the influence on the driving behaviour of various objects that could be encountered along the road. These can be classified in static (signage, stationary vehicles at the roadside, etc.) and dynamic objects (cars, motorcycles, bicycles). The aim of this paper is to propose a proper analysis regarding the driver’s visual behaviour. In particular, the authors examined the quality of the visually informa-tion acquired from the entire road environment, useful for detecting any critical safety condition. In order to guaran-tee a deep examination of the various possible behaviours, the authors combined the several test outcomes with other variables related to the road geometry and with the dynamic variables involved while driving. The results of this study are very interesting. As expected, they obviously confirmed better performances for the ‘isolated vehicle’ in a rural two-lane road with different traffic flows. Moreover, analysing the various scenarios in the disturbed condition, the proposed indices allow the authors to quantitatively describe the different influence on the visual field and effects on the visual behaviour, favouring critical analysis of the road characteristics. Potential applications of these results may contribute to improve the choice of the best maintenance strategies for a road, to select the optimal signage location, to define forecasting models for the driving behaviour and to develop useful instruments for intelligent transportation systems.

Sign in / Sign up

Export Citation Format

Share Document