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Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic...
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... ITS techniques and technologies can reduce energy consumption by changing the driving behavior, suggesting congestion free smooth path, automatic traffic control signal, electronic toll collection and platooning. From the mechanical properties of the vehicle the automobile engineer proved that the vehicle running 50-70km/h for gasoline engines and 50-80km/h for the petrol engine consumed lowest rate of fuel. Fig. 1 illustrates the basic relationship of the vehicle speeds with the fuel consumption from which exhaust pollutant by the driving pattern can be assumed [24, 25]. By eliminating the congestion and suggesting an uninterrupted path with the aid of ITS technique the vehicle can maintain this green speed and then obtain the best fuel efficiency and pollution at minimum level [26]. If the vehicle drives above green speed or run bellow the green speed it will consume more fuel [27]. The curve C in Fig. 1 shows that if the aerodynamic drag is reduced at high speed, then fuel consumption will also be reduced [28]. The speed versus fuel consumption for the hybrid and electric vehicle ...
Context 2
... ITS techniques and technologies can reduce energy consumption by changing the driving behavior, suggesting congestion free smooth path, automatic traffic control signal, electronic toll collection and platooning. From the mechanical properties of the vehicle the automobile engineer proved that the vehicle running 50-70km/h for gasoline engines and 50-80km/h for the petrol engine consumed lowest rate of fuel. Fig. 1 illustrates the basic relationship of the vehicle speeds with the fuel consumption from which exhaust pollutant by the driving pattern can be assumed [24, 25]. By eliminating the congestion and suggesting an uninterrupted path with the aid of ITS technique the vehicle can maintain this green speed and then obtain the best fuel efficiency and pollution at minimum level [26]. If the vehicle drives above green speed or run bellow the green speed it will consume more fuel [27]. The curve C in Fig. 1 shows that if the aerodynamic drag is reduced at high speed, then fuel consumption will also be reduced [28]. The speed versus fuel consumption for the hybrid and electric vehicle ...
Citations
... Traffic signal control systems play a crucial role in enhancing the efficiency of urban networks by managing traffic flow and improving safety for all road users [2]. By controlling the flow of traffic at intersections and crosswalks, traffic signals help reduce the number and severity of accidents, minimize travel time, and improve air quality by reducing fuel consumption and emissions from stop-and-go vehicles [3][4][5][6]. ...
In the realm of traffic signal operations, the Signal Timing Manual second edition (STM2) serves as a foundational guide for professionals engaged in multimodal signal retiming projects. However, it is acknowledged that the STM2 has its limitations, and real-world conditions often necessitate adaptations in the established procedures. Considering this context, this research endeavors to bridge this gap by conducting a comprehensive survey aimed at traffic signal professionals. This study presents the findings of a comprehensive survey conducted among traffic signal professionals to explore the methodologies, challenges, and practices involved in multimodal signal retiming projects. The survey aimed to obtain detailed insights into the current state of signal retiming, the types of data and tools utilized, and the adaptations necessary to address the complexities of multimodal urban transportation networks. The survey highlights and summarizes responses from 36 professionals across North America, providing insight into both the common strategies and unique challenges faced by those responsible for optimizing signal timings in diverse and dynamic urban environments. The survey results reveal a reliance on diverse tools and data types for signal optimization, highlighting the complexities of accommodating different transportation needs. The findings underscore the importance of tailored approaches and advanced technologies in enhancing signal retiming processes. The insights gained from this study will inform future strategies and enhance the effectiveness of signal retiming procedures in urban areas, thereby contributing to improved traffic management and multimodal transportation efficiency.
... Additionally, bikes are incredibly fuel-efficient, which means people can save money through spending less on fuel (Nasir et al., 2014). Although in the past decade, people only tended to use it to meet their specific needs, now having a motorbike has become a lifestyle for the technological change in the motorbike industry (Azmy et al., 2020). ...
The global economy is expanding in line with the revolution in technology and informatics. In parallel, the global automotive industry has extended into every region of the world. Nowadays, the usage behavior toward automotive vehicles is gathering significant attention in developing countries where motorbikes hold the top position for their light weight, fuel efficiency, and low cost in terms of adoption features compared to other automobiles. As it is one of the most densely populated cities, the people of Dhaka hold different mindsets toward purchasing motorbikes. This study attempts to unlock the mystery behind the purchase intention of Dhaka city customers in relation to motorbikes. For that purpose, a literature review was carried out and a conceptual framework was developed by adopting and modifying the Theory of Planned Behavior (TPB). A quantitative research approach was undertaken, and 351 responses were collected by using the snowball sampling method. Structural Equation Modeling (SEM) was conducted by using SmartPLS 4. Through our analysis, only one predictor, service quality, was found as the determinant factor behind the purchase intention of motorbikes. Based on the findings, the author determined the implications for practitioners to better understand the market and included some solutions. Lastly, limitations and future research possibilities were discussed.
... Considering the connection between fuel consumption and air pollution in urban and suburban areas, several estimation models for vehicle emissions have been developed [15][16][17]. Overall, there is a wide consensus that the road vertical profile (i.e., longitudinal slope) is one of the major road design factors controlling the rate of fuel consumption and/or vehicle emissions [11,[17][18][19]. Supplementary to this aspect, the impact of the horizontal profile of a roadway can be indirectly considered through the travelling speed. ...
... Modelling 2024, 5, FOR PEER REVIEW 3 Figure 1. Fuel-speed relationship for thermal and eco-friendly vehicles (adapted from [19]). Of course, the factor of vehicle weight can significantly alter the green range of optimal speeds. ...
... Simultaneously, the application of intelligent transportation systems will help the promotion of fuel consumption reduction according to two main strategies [19]. These include: (a) the attempt to reduce congestion and traffic delays thanks to connectivity and the adoption of optimal vehicle speeds, and (b) the ability of drivers to follow a greener route in terms of energy and fuel efficiency. ...
A roadway path is most commonly perceived as a 3-D element structure placed within its surrounding environment either within or outside urban areas. Design guidelines are usually strictly followed to ensure safe and comfort transportation of people and goods, but in full alignment with the terrain configuration and the available space, especially in urban and suburban areas. In the meantime, vehicles travelling along a roadway consume fuel and emit pollutants in a way that depends on both the driving attitude as well as the peculiar characteristics of road design and/or pavement surface condition. This study focuses on the environmental behavior of roadways in terms of fuel consumption, especially of heavy vehicles that mainly serve the purpose of freight transportation within urban areas. The impact of horizontal and vertical profiles of a roadway structure is theoretically considered through the parameters of speed and longitudinal slope, respectively. Based on theoretical calculations with an already developed model, it was found that the slope plays the most critical role, controlling the rate of fuel consumption increase, as an increase ratio of 2.5 was observed for a slope increase from 2% to 7%. The variation was less intense for a speed ranging from 25 to 45 km/h. The investigation additionally revealed useful discussion points for the need to consider the environmental impact of roadways during the operation phase for a more sustainable management of freight transportation procedures, thereby stimulating an ad hoc development of fuel consumption models based on actual measurements so that local conditions can be properly accounted for and used by road engineers and/or urban planners.
... Times of sunrise and sunset change substantially throughout the seasons in northern BC, but times of peak traffic flow remain more constant. Generally, peak volumes of traffic flow are between 06:00-09:00 h and 16:00-19:00 h, regardless of the time of year (Nasir et al., 2014;FWHA, 2017). However, we found most moose visits occurred outside of peak traffic volume hours, despite most detections occurring in summer months when traffic volume is higher (BC MoTI, 2012). ...
... Similarly, irregular driving habits can lead to vehicle malfunction which will trigger high carbon emission (Bikam, 2021). Aggressive driving pattern such as inefficient gear changing in older vehicles can lead to engine to malfunction and failure , resulting increase in carbon emissions (Nasir et al., 2014). Whereas for new vehicles, automatic transmission of gear changes has been designed to reduce negative impact of emissions and aggressive driving. ...
... The items are constructed based on previous literature studies. The items were adapted from sources such as (Bikam, 2021;Nasir et al., 2014Sugathapala & Gajanayake, 2019. Item one to three is constructed based on Sugathapala & Gajanayake (2019), focus on the speed and acceleration profile of vehicle which contributes to vehicle emissions. ...
... Item four and five is derived based on Bikam (2021), emphasize that aggressive or irregular driving behavior can lead to misuse of vehicle and thus, increase in vehicle emission. Next, item six is constructed based on Nasir et al (2014), claimed that unnecessary gear changing in old vehicles can result increase in carbon emission. Based on the mean value, the finding of this study shows the overall level of respondent's agreement towards statement on driving pattern is high. ...
A questionnaire plays an important part in research for collecting pertinent data regarding the research study. It is important to design questionnaires meticulously to minimize errors. However, researchers often face challenges in designing effective questionnaires which results in biased findings. Therefore, this paper aims on the development of questionnaires items on factors contributing to old vehicle emissions in urban areas. The questionnaire items designed consist of information on studied variables such as fuel consumption, driving pattern, mobility behavior, maintenance and repair and vehicle age and carbon emissions. The questionnaires items were designed based on past literature studies. As a result, a total of thirty-six items were constructed in this study. This study uses the Five Point Likert Type Scale which reflects the respondent’s agreement towards the statement in the questionnaires. The study also seeks to provide insight to the government and policy makers on factors that contribute to old vehicle emissions in urban settings.
... First, the energy efficiency of the engine was extremely poor, converting at best 27 % of the chemical energy in the fuel to movement (including internal parts of the engine), which means that 73 % or more was lost as heat (Albatayneh et al., 2020). Second, optimal energy conversion occurred at speeds between 50 km/h and 80 km/h, which was outside the range of normal operation (Nasir et al., 2014). Therefore, cars operated mostly at inefficient conversion energy speeds, making energy inefficiency even worse. ...
... Application to reduce emissions AI can optimize traffic flow, improve route planning, and promote the use of electric vehicles, resulting in reduced greenhouse gas emissions and air pollution [24,25,26,27,28] Application to achieve energy efficiency AI algorithms can optimize vehicle operations, reduce fuel consumption, and improve energy efficiency, leading to lower energy-related environmental impact [29,20,30] Application to obtain sustainable mobility AI applications can support the development of sustainable transportation solutions, such as ride-sharing services and intelligent transportation systems, which reduce the overall environmental impact of transportation activities [31,32,33,34] Construction Application to achieve energy efficiency AI can optimize building energy management systems, monitor energy consumption, and identify opportunities for energy efficiency improvements, reducing the environmental impact associated with construction energy use [35,36,37] Application to get waste reduction AI tools can analyze construction data to identify opportunities for waste reduction, material recycling, and improved resource management, leading to reduced environmental impact through minimized construction waste generation [38,39] Application to carry out sustainable materials AI can assist in the selection of sustainable building materials, considering factors such as embodied carbon, recycled content, and environmental certifications, thus promoting environmentally friendly construction practices [40,41] cont. Table 2 Office services Applications for energy management AI technologies can optimize energy consumption in office buildings by monitoring and controlling HVAC systems, lighting, and equipment usage, resulting in energy savings and reduced environmental impact [42,43,44] Applications for waste management AI-powered systems can improve waste sorting, recycling, and disposal processes in office environments, contributing to reduced landfill waste and promoting sustainable waste management practices [45,46] Virtual Collaboration AI tools enable remote collaboration and virtual meetings, reducing the need for business travel and associated carbon emissions, leading to a positive environmental impact [47] Chemical industry Process Optimization AI can optimize chemical manufacturing processes by analyzing data and identifying opportunities for energy and resource efficiency improvements. ...
Environmental management systems (EMS) are essential in promoting sustainable practices and mitigating the adverse effects of human activities on the environment. As technology continues to advance, there is an increasing opportunity to utilize advanced technologies to improve environmental management systems. This article examines the potential of different advanced technologies, such as artificial intelligence (AI), blockchain, big data, and the Internet of Things (IoT), within the context of environmental management systems. This article intends to offer valuable insights to researchers, practitioners, and policymakers by examining the potential uses of AI, blockchain, big data, and IoT in environmental management systems. The goal is to demonstrate how these advanced technologies can be leveraged to enhance sustainability, boost environmental performance, and yield favourable environmental results across different sectors and industries.
... Acceleration is an important feature to consider, as it alone can define driving aggressiveness [25], negative impacts on other vehicles [24], and fuel efficiency [60]. Acceleration is calculated using (6). ...
... Our reward function considers driving aggressiveness and penalizes the harsh action to overcome these problems. According to Nasir et al., "The best way to maintain the engine in low speed and high torque mode is to select the highest speed ratio" [60]. It is considered efficient driving if the driver does not change acceleration frequently [61]. ...
Artificial intelligence (AI) in autonomous vehicles (AVs) is gaining much focus on safety, comfort, and efficiency. The goal is to provide the driver with assistance and mimic human driving. Deep reinforcement learning (DRL), specifically the deep deterministic policy gradient (DDPG) is often used for longitudinal velocity control, utilizing the velocities of the vehicles and the distance between them. DDPG has been found effective in many studies when it is embedded with a self-supervised learning (SSL) method. In this paper, a framework for longitudinal velocity control is proposed using SSL and DDPG frameworks. The inputs of the DDPG networks have been replaced by the outputs of the SSL network. The primary objective of this SSL is to enable the model to accurately predict future states based on current states and actions. The input features of the datasets have been modified so that the DDPG model can get additional information that helps the model make better predictions. These features include the distance between vehicles, ego vehicle velocity, acceleration, jerk, and lead vehicle relative velocity, and estimated velocity. Furthermore, a custom reward function is designed to account for safety, driving comfort, negative impact, driving aggressiveness, and fuel efficiency. In order to evaluate the model, the algorithm has been trained and tested on a variety of datasets, including simulated and real-world data. The analysis demonstrates that the new architecture maintains strong robustness across various datasets and outperforms the current state-of-the-art models.
... The overall capacity of the intersection is constrained due to that traffic flow is associated with idleness as vehicles make frequent stops. Such scenarios lead to traffic congestion, crashes, fuel wastage, and exhaust emissions [1]. In developing countries, the situation is compounded by the limited availability of land and financial constraints for infrastructure expansion and traffic light systems maintenance. ...
Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.
... It has been proven that the impact of traffic flow on traffic emissions is significant. According to Nasir et al. [9], traffic flow condition including the average speed, traffic congestion condition, and idle time had a strong correlation with the fuel consumption in urban area. The traffic data is usually modelled using traffic modelling software including AIMSUN (Advanced Interactive Microscopic Simulator for Urban Networks) and TRANSYT (Traffic Network Study Tool Version). ...
Greenhouse gas emissions from transportation systems have become a grave health and environmental concern as the rise in population and urban connectivity has caused the increase in traffic demand and congestion. Increasing the use of public transportation would become a key factor in mitigating the adverse results of vehicular emissions. In this work, we have constructed a traffic and fuel consumption forecasting framework to analyze the effects of increasing public transportation modal share on the reduction of the total average fuel consumption by daily traffic. We have successfully forecasted a fuel savings of 21% via the increase of public transportation modal share from 12% to 40%. Our results could serve as an effective guideline for policymakers in drafting sustainable transportation masterplans.
