City Traffic Conditions Research Articles

A transportation Revitalization index prediction model based on Spatial-Temporal attention mechanism

The global outbreak of COVID-19 has had a substantial impact on various sectors worldwide, including the economy, healthcare, entertainment, policy formulation, and international relations, with the transportation industry being particularly hard-hit. To curb the widespread transmission of the virus, many regions globally have implemented policies and measures to restrict transportation. These actions not only directly affect the transportation industry but also further impose a severe impact on the economy and societal development of various areas. In this context, the Transportation Revitalization Index (TRI) becomes particularly important. It can evaluate the degree of recovery of city traffic conditions after the pandemic, and accurate prediction of TRI can help governments and decision-makers respond more precisely to the challenges that the pandemic brings to the transportation industry. However, existing research primarily focuses on the direct correlation between TRI change data and COVID-19 pandemic data, without fully considering the dynamic spatial correlation features and time dependency features that affect the nonlinear changes of TRI. In light of the above situation, this study proposes a Deep Spatial-Temporal prediction model based on the Attention Mechanism (DeepST-AM). The DeepST-AM deeply integrates historical TRI data with multivariate pandemic information and uses a spatial–temporal attention mechanism to capture the deep and complex spatial–temporal information of urban data. To more accurately capture the long-term complex features of TRI data, this paper designs a Gaussian temporal convolution model dedicated to TRI data. To validate the effectiveness of DeepST-AM, researchers used real data from 29 core cities in China as samples and compared the performance of DeepST-AM with existing multiple methods on TRI prediction tasks. The experimental results showed that compared to other methods, the DeepST-AM proposed in this paper has a significant advantage in the long-term prediction tasks of TRI in terms of performance evaluation, indicator prediction, etc. In summary, this research provides a more accurate and comprehensive prediction model for the traffic recovery status after the pandemic, hoping to provide strong support for future decisions.

The Hybrid Trip Destination Prediction Model of Vehicles Based on Autoencoder and High-Order Interaction Features

Through the intelligent vehicles trip data collection, processing and analysis, so as to predict the vehicles trip destination, this technology can improve the user's driving experience and the city traffic conditions. The vehicles dispatching system can also judge the real-time road conditions according to the prediction results and plan a more reasonable and efficient driving route, which is of great significance to urban traffic planning and urban construction planning. However, the amount of information in the vehicles data is less, which cannot meet the training needs of some artificial intelligence models. In addition, due to communication technology and other issues, there is a certain degree of deviation between the GPS data of the vehicles and the real data. The previous vehicles destination prediction model did not well eliminate the negative impact of this deviation. In addition, the previous model linearly adds the characteristics of each vehicles's trip data, which cannot reflect the complex rules of the vehicles's trip destination. Therefore, to address the above-mentioned drawbacks, we propose a novel vehicle trip destination prediction method named Hybrid Trip Destination Prediction Model of Vehicle Based on Autoencoder and High-Order Interaction Features (HAHIF). The HAHIF model extracts robust hidden features using the autoencoder model and considers the second-order association between them using a factorization machine to improve its superiority and effectiveness. Compared with mainstream benchmarks, the HAHIF model has an MSE value of 0.096, an RMSE value of 0.427, and a MAE value of 0.203 on the public dataset, both of which are the first place, which verifies that the HAHIF model has good predictive ability.

Test bench setup for emulating electric vehicle on-road conditions

This work provides an electric vehicle (EV) test bench with a motor/dynamometer setup with a battery and ultracapacitor (UC) hybrid energy storage. Extending the driving range and enhancing the battery life are the main concerns with EVs nowadays. The drive motor (DM) used is a three-phase induction motor (IM), which is controlled by the current sensorless indirect vector control method. This fault-tolerant method can be used in the case of current sensor failure. All the resistive forces acting on an EV are emulated based on the concept of vehicle equivalent rotational inertia for different real-life driving cycles, namely ECE R15 and NYCC. The battery alone is not capable of handling the surge current requirement during the acceleration period and is not able to capture the total power during regenerative breaking as it has a low power density. Hence, a high power density source like UC is a must to handle these conditions during frequent stop and go city traffic conditions as per ECE R15 and NYCC driving cycles. This EV emulation technique helps in getting the DC-link power profile for an EV for any driving scenario. Hence, it is possible to test the different powertrain components of an EV using this proposed technique.

Surface undulation parameters of continuously variable transmission friction during turning

City traffic conditions contain frequent starts and stops. Thus, Single-Stage Continuously Variable Transmissions are subject to high loads when operating at high gear ratios, leading to excessive friction that cause slipping and heating of friction elements. This study proposes a microprofile with undulation on the surface of the pulley. A mathematical dependence on the technological quality assurance of the surface layer of the V-belt transmission parts formed during turning is proposed, enabling the prediction of height parameters of undulation depending on the change in the spindle speed, feed, and cutting speed. Estimating the error in the field experiment data and calculating values of the proposed methodology showed that the height parameters of the undulation of pulleys have a relative error of 6.3%.

A Smart Cross-System Framework for Joint Allocation and Scheduling With Vehicle-to-Grid Regulation Service

The growing popularity and penetration of electric vehicles (EVs) bring in challenges and opportunities to the power grid. They represent a huge aggregate electricity consumption from the city power grid, while it is possible to make use of their batteries for supporting vehicle-to-grid (V2G) regulation service. Besides, there is a need to coordinate a collection of EVs in an intelligent manner through the smart transportation system in a smart city. In this paper, we propose a novel smart cross-system framework to support V2G regulation service in a smart city. Specifically, by considering electric buses (EBs) as a particular group of EVs with large battery packs installed and predictable operation schedules, we jointly solve the problems of EB allocation and scheduling with V2G regulation service. First of all, by using the deep learning approach, the short-term city traffic conditions can be predicted via the gated recurrent units (GRUs) neural network. With the predicted traffic conditions, we then formulate the EB allocation and V2G scheduling as a mixed-integer quadratic programming (MIQP) problem. In order to solve this problem effectively, we utilize the Lagrangian dual decomposition method to decouple the main problem into subproblems for EBs and devise a distributed algorithm to solve each subproblem. The simulation results show that our proposed approach is effective in EB allocation and V2G scheduling for the regulation service. Through efficient EB routing strategies under accurate predicted traffic conditions, the power fluctuations of the city power grid can be well flatten by providing the V2G regulation service.

Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We model ATSC problem as a partially observable weak cooperative traffic model (PO-WCTM). Different from a traditional IRL task that averages the returns of all agents in fully cooperative games, the learning goal of each intersection in PO-WCTM is to reduce the cooperative difficulty of learning, which is also consistent with the traffic environment hypothesis. To achieve the optimal cooperative strategy of PO-WCTM, we propose an IRL algorithm called Cooperative Important Lenient Double DQN (CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the forgetful experience mechanism and the lenient weight training mechanism. The former mechanism decreases the importance of experiences stored in the experience reply buffers, while the latter mechanism increases the weight experiences with high estimation and `leniently' trains the DDQN neural network. Experiments in two real traffic scenarios and one simulated traffic scenarios show that, CIL-DDQN outperforms other methods in almost all performance indicators of ATSC.

Revisiting some important heritage bridges of Calcutta, India

This paper attempts to cover the planning, design and construction of five bridges in Calcutta, India, reconstructed after World War I and still in operation, constructed across the city’s Circular Canal and Tolly Nullah. These bridges were reconstructed with an aim to replace the existing bridges at these locations, which were unsuitable due to the emerging city traffic conditions. All these bridges are still in operation (except Chitpore Bridge, which is currently under inspection for retrofitting). The five bridges covered are the twin-leaf bascule bridge at Kidderpore, Tollygunge Bridge crossing Tolly Nullah, Beliaghata Bridge across the Circular Canal, Alipore Bridge across Tolly Nullah at Alipore and Chitpore Bridge over the Circular Canal. The first one is a twin-leaf rolling lift bascule structure with counterweights disposed below the roadway level. The bridges at Tollygunge, Alipore and Chitpore are of reinforced-concrete bowstring type. Beliaghata Bridge is a reinforced-concrete three-hinged arch-type bridge. These bridges were mostly supported on piled foundations with innovative solutions adopted to tackle the poor-quality alluvial Calcutta soil at these sites. Advanced construction techniques were adopted considering the type of traffic, existing utilities and available space at construction sites. This paper converges to their present condition and tries to identify possible rehabilitation methods.

Ocena zastosowania napędów hybrydowych w warunkach ruchu miejskiego

The conditions of use of the vehicle significantly affect the performance results. Traffic conditions in a specific city directly affect the consumption of energy, fuel and emissions of harmful compounds in exhaust fumes. Conduction of the measurements of a vehicle’s performance parameters in operating conditions is very troublesome and is often not possible to realize. An alternative is to use the simulation programs. Vehicle simulation programs offer options related to vehicle models or drive unit components and allow development of new models. Based on the results of simulation testing, it is possible to analyse the level of fuel and energy consumption as well as emissions of harmful compounds in exhaust gases and the operating effectiveness of the drive system in the speed profile. The paper presents the evaluation of the effectiveness of using hybrid electric drive system in passenger cars in medium-sized city traffic conditions using the Kielce example. The simulation tests were based on the speed profiles recorded during real-world test drives in various times of the day. The simulation results were used to conduct an analysis of fuel consumption and pollutant emissions recorded by conventional and hybrid vehicles.

Performance analysis of a hybrid lightweight vehicle with downsized engine

ABSTRACTToday, due to increased environmental concerns on NOx and PM emissions caused by diesel vehicles, researchers have focused on the development of hybrid engine vehicles. Therefore, an SI engine was chosen in the present study. In addition, an extra downsized single-cylinder engine model for hybrid system and a four-cylinder engine model for conventional system were selected with lightweight vehicle in order to investigate advantageous provided in crowded city traffic conditions. The models have three-way catalyst (TWC) and cold-start operation conditions. The result of the experiment using the theoretical model developed by us and the experimental results of the SI engine are verified by using a series hybrid drive system in a gasoline light-duty vehicle used today as a passenger car. According to obtained results, CO, HC, and NOx emissions of the four-cylinder gasoline fueled vehicle and single-cylinder series hybrid system were compared instantaneously for ECE, NEDC, and WLTC cycles. According to results, fuel consumption, CO, and HC decreased while NOx emissions increased.

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

Potential of Implementing Urban Freight Strategies in the Accommodation and Food Services Sector

This paper studies freight demand patterns of establishments in the accommodation and food services sector, and the potential of implementing urban freight strategies in this sector to improve local and city traffic conditions. This research uses data from the City of Stockholm and assesses the potential implementation of two strategies (i.e., consolidation and off-peak hour deliveries) proposed in The Stockholm Freight Plan to foster safe, clean, and efficient freight deliveries. The results show that accommodation establishments have different freight generation patterns and opinions about the strategies proposed than other food services establishments. The results also show that the strategies proposed have good acceptance among 20% of the receivers surveyed, which would facilitate the implementation of the strategies and lead to significant benefits both at the local and city levels.

Comparison of fuel consumption between a vehicle with standard and hybrid drive system

The article presents systems of alternative vehicle drive. Fuel consumption tests were performed between a car with a traditional drive, and hybrid in city traffic conditions. The research was carried out on a selected route, located within the agglomeration of the city of Krakow. The aim of the research was to determine the difference in fuel consumption of compared cars in real urban traffic conditions. The tests were carried out at different times of the day, which resulted in different traffic volumes.

Correction to: The Analysis of Exhaust Gas Thermal Energy Recovery Through a TEG Generator in City Traffic Conditions Reproduced on a Dynamic Engine Test Bed

Correction to: The Analysis of Exhaust Gas Thermal Energy Recovery Through a TEG Generator in City Traffic Conditions Reproduced on a Dynamic Engine Test Bed

Analysis of an Increase in the Efficiency of a Spark Ignition Engine Through the Application of an Automotive Thermoelectric Generator

We have analyzed the increase of the overall efficiency of a spark ignition engine through energy recovery following the application of an automotive thermoelectric generator (ATEG) of our own design. The design of the generator was developed following emission investigations during vehicle driving under city traffic conditions. The measurement points were defined by actual operation conditions (engine speed and load), subsequently reproduced on an engine dynamometer. Both the vehicle used in the on-road tests and the engine dynamometer were fit with the same, downsized spark ignition engine (with high effective power-to-displacement ratio). The thermodynamic parameters of the exhaust gases (temperature and exhaust gas mass flow) were measured on the engine testbed, along with the fuel consumption and electric current generated by the thermoelectric modules. On this basis, the power of the ATEG and its impact on overall engine efficiency were determined.

Discovering and Visualizing Underlying Traffic Regions from Vehicle Trajectories with Multi-Features

City traffic often exhibits regional characteristics, such as large trucks frequently appearing in the suburbs, and the paths to playgrounds on weekends generally being congested. Discovering and visualizing these hidden traffic regions inside which roads share similar characteristics of traffic conditions simplifies the modeling complexities of whole city traffic conditions and therefore contributes significantly toward city planning. Unfortunately, such traffic regions always have irregular shapes and are time varying, which makes their discovery extremely complicated. In addition, establishing a method to visualize and explore the traffic regions interactively still remains challenging. In this article, the authors propose a latent Dirichlet allocation (LDA)-based approach to the discovery of underlying traffic regions (or region topics) from vehicle trajectories captured by surveillance devices installed along roadsides. They treat vehicle trajectories as documents and the values of different traffic features, such as locations, directions, speeds and vehicle types, as the corresponding words. After applying the LDA model, they obtain a list of region topics with combined feature values, in which the different feature values are clustered with probabilistic assignments. Meanwhile, they build a prototype system to explore the surveillance-device-based vehicle trajectories according to the discovered region topics. The prototype system, which consists of map view, cloud view, treemap view and matrix-table view, visualizes the feature values of hidden traffic regions. The authors finally research a real case based on the traffic data in Wenzhou City, a large city in eastern China with a population of more than nine million. They investigate approximately 157 surveillance devices and 750,000 moving vehicles. The case demonstrates the effectiveness of both their proposed approach and the prototype system.

Integrating Streaming Data into Semantic Mashups

Abstract Streaming data, i.e., frequently changing and potentially infinite data flows, are becoming more and more prevalent in today's interconnected world. In a Smart City context, for instance, citizens can access real-time information about a city's weather, pollution, or traffic conditions. However, turning vast real-time data streams into actionable knowledge poses significant challenges. For instance, heterogeneous data from varied sources needs to be normalized and integrated, ideally using a common structured vocabulary. Furthermore, to provide users with both real-time and aggregate views on the data, data stream access requires support for temporal operators. Finally, it is difficult for non-technical users to integrate and leverage streaming data. To tackle these challenges, we propose an extension of the Linked Widgets concept for the streaming data domain. In this paper, we outline a platform prototype for the creation of streaming data mashups that is currently under development and illustrate its potential by means of a use case based on city real time bike data from the city of Vienna.

The Analysis of Exhaust Gas Thermal Energy Recovery Through a TEG Generator in City Traffic Conditions Reproduced on a Dynamic Engine Test Bed

We present an analysis of thermal energy recovery through a proprietary thermoelectric generator (TEG) in an actual vehicle driving cycle reproduced on a dynamic engine test bed. The tests were performed on a 1.3-L 66-kW diesel engine. The TEG was fitted in the vehicle exhaust system. In order to assess the thermal energy losses in the exhaust system, advanced portable emission measurement system research tools were used, such as Semtech DS by Sensors. Aside from the exhaust emissions, the said analyzer measures the exhaust mass flow and exhaust temperature, vehicle driving parameters and reads and records the engine parameters. The difficulty related to the energy recovery measurements under actual traffic conditions, particularly when passenger vehicles and TEGs are used, spurred the authors to develop a proprietary method of transposing the actual driving cycle as a function V = f(t) onto the engine test bed, opn which the driving profile, previously recorded in the city traffic, was reproduced. The length of the cycle was 12.6 km. Along with the motion parameters, the authors reproduced the parameters of the vehicle and its transmission. The adopted methodology enabled high repeatability of the research trials while still ensuring engine dynamic states occurring in the city traffic.

Comparison of Quality of Braking Pads under Operational Conditions

Taking into account vehicle safety, braking systems and their parts are subject to strict evaluation criteria not only due to market competitions of manufacturers but also from the customer point of view. The paper is focused on a subject research by comparing the qualitative properties of friction braking materials of vehicles under operational conditions considering the customer position. The research results containing the comparison and evaluation of friction braking materials could address the manufacturers who affect the product quality and complex manufacturing processes on the basis of real operational conditions. The final analysis and evaluation of braking pads was carried out on the basis of their wear directly under operational conditions by monitoring the material shortage and surface roughness of the friction material. We have selected and compared three types of friction materials within our research work which were applied to the Škoda Fabia 1,2 car under city traffic conditions. The research results lay stress on economic and qualitative indicators by selecting the correct braking pads taking into account the safety of vehicle operation.

Emission profiling of diesel and gasoline cars at a city traffic junction

In congested urban roads, cars must stop at intersections because of city traffic lights. As a result, pedestrians and traffic police personnel are exposed to pollutants emanating from the tailpipe of various vehicles at such city traffic junctions. In this study, various gasoline- and diesel-fueled cars complying with different emission standards were tested for their emissions in simulated city traffic junction conditions. The engine exhaust from these cars was subjected to physicochemical characterization at different engine speeds under no-load conditions. These engine conditions were chosen because the cars idle at different engine speeds at a city traffic junction. Gravimetric and real-time measurements were performed for the tailpipe exhaust sampled from these vehicles. Exhaust particles were collected on 47mm diameter quartz filter papers and subjected to gravimetric analysis for determining the total particulate mass (TPM) and trace metals while the engines were operated at two different engine idling speeds, 1500rpm (representing low idling) and 2500rpm (representing high idling). At similar engine operating conditions, TPM and trace metals were lower for the exhaust from gasoline engines compared to the exhaust from diesel engines. Real-time measurements were performed for particle-bound poly-aromatic hydrocarbons (PAHs), particle number and size distribution, regulated gaseous emissions and smoke opacity of the exhaust at four different engine speeds, 1500, 2000, 2500, and 3000rpm. Particle-bound PAHs showed a decreasing trend for the vehicles that complied with stricter vehicular emission standards. Higher particle peak number concentrations were observed for diesel exhausts compared to the results for gasoline exhaust. Regulated gaseous emissions were also compared.

Study on the economic and environmental benefits of different EV powertrain topologies

• EV powertrain topologies can be realized by flexible distributed electric motors. • Model-based design optimization method is used to analyze and evaluate the EV. • The wheel-hub drive with reducer possesses the most competitive advantages. • US06 and NYCC result in the deterioration of EVs’ cost and GHG emissions. Numerous feasible schemes of powertrain topology can be designed for the electric vehicles (EVs) based on the distributed configurations of the electric motors. In this study, the effects of different EV powertrain topologies on the energy efficiency, vehicle ownership cost and lifecycle greenhouse gas (GHG) emissions of EVs are investigated. Energy-based vehicle simulation model including the regenerative braking function and battery degradation prediction method is established firstly. An optimization scheme combining the energy-based vehicle simulation model is conducted to minimize the electric energy consumption under various scenarios and driving conditions. Then the vehicle ownership cost and lifecycle GHG emissions of EVs are evaluated based on Chinese EV market and electricity grid. The sensitivity analyses of EV powertrain topology are implemented based on the different vehicle weights, CO 2 intensities of electricity and all-electric ranges. Results show that EVs using the powertrain of wheel-hub drive with the gear reducer have lower energy consumption. Furthermore, the driving cycles with more aggressive acceleration/deceleration and frequently stop-and-go conditions can increase both the vehicle ownership cost and lifecycle GHG emissions simultaneously. Chinese city traffic conditions will help EVs to obtain more benefits in respect of the economy and environment.