This research develops an optimization model for eco-driving at signalized intersection. In urban areas, signalized intersections are the “hot spots” of air emissions and have significant negative environmental and health impacts. Eco-driving is a strategy which aims to reduce exclusive fuel consumption and emissions by modifying or optimizing drivers’ behaviors. With the help of vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure communication (V2I), eco-driving could utilize the signal phase and the queue-discharging time information to optimize the speed trajectories for the vehicles approaching an intersection in order to reduce fuel consumption and emissions. A few research studies have been conducted on the development of algorithms that utilize traffic signal information to reduce fuel consumption and emissions. Hence, the goal of this research is to develop an optimization model to determine the optimal eco-driving trajectory (the speed profile) at a signalized intersection, which aims to achieve the minimization of a linear combination of emissions and travel time. Then enumeration method, simplex optimization and genetic algorithm are investigated to determine a practicable and efficient method to solve the proposed optimization problem. As various scenarios of distance from the vehicle to the intersection, queue discharging time and weights of emission/travel time will lead to different optimal trajectories and different emissions and travel times. A sensitivity study is conducted to analyze and compare the performance of the optimal solution in various scenarios of different such parameters. In addition, a baseline study is conducted to investigate the benefits of eco-driving when drivers only decelerate in advance but not apply the recommended speed trajectory. The results of case study show that genetic algorithm is a preferred method to solve the proposed optimization problem; Eco-driving could achieve satisfied reduction in emissions without significantly increasing travel time and emissions is more sensitive to various scenarios than travel time; Eco-driving still could achieve reduction in emissions as long as the drivers decelerate earlier even though the they would not apply the recommended speed trajectory under certain conditions.

Context: In this work a system, the eco service, is developed that incorporates elements from gamification to help drivers adapt to a more energy-efficient driving style. An energy-efficient driving style can help reduce fuel consumption, increase traffic safety and help reduce the emissions made from vehicles. Objectives: The main goal of this work is to explore ways of how gamification can be used in the context of eco-driving. Evaluating different elements and how they work in this context is important to help drivers to continue improving their driving style. Method: The eco service was tested on 16 participants where each participants was asked to drive a predetermined route. During the experiment the participants were given access to the eco service in order to gain feedback on their driving. Lastly interviews were held with each participant on questions regarding the use of gamification and how it can be improved in the context of eco-driving. The research was done in collaboration with a swedish company, Swedspot AB, that works with software solutions for connected vehicles. Results & Conclusions: Positive results were found on the use of gamification. Participants reported that the eco service made them more aware of their driving situation and how to improve. Game elements with positive influence were reward and competitive based and helped motivate the driver to improve.

Användandet av simulatorer i utbildningar ökar mer och mer. Simulatorer har använts inom pilotutbildningar och inom medicinsk utbildning länge och det finns mycket forskning som stödjer deras effektivitet. Nu har simulatorerna blivit mer tillgängliga i och med den tekniska utvecklingen och har börjat användas för förarutbildningar. Däremot saknas samma gedigna vetenskapliga stöd som finns för pilotutbildningar och medicinsk utbildning. Det finns visst underlag för utbildning i riskmedvetenhet men inte så många andra färdigheter. Syftet med studien var att undersöka hur effektiv en simulator är vid utbildning av förare i Eco-driving. Till studien rekryterades 20 elever från Yrkesakademin som utbildas för behörighet C, tung lastbil. Studien var av mellangruppsdesign där experimentgruppen tränade Eco-drivingfärdigheter och data över bränsleförbrukning och hastighet samlades in. Kontrollgruppen fick en teoretisk utbildning i Eco-driving i form av en inspelad video. Experimentgruppen hade en signifikant förbättring av bränsleförbrukning men inte kontrollgruppen. Detta stödjer effektiviteten av simulatorbaserad utbildning av Eco-driving. Resultaten är även uppmuntrande till träning av liknande färdigheter som bland annat är av betydelse för trafiksäkerhet. Dessutom finns det goda möjligheter att minska kostnaderna vid förarutbildningar samtidigt som eleverna lär sig bättre. / The use of of simulators in education is increasing. The aviation and medical education have a long history of implementing simulator training and education. With a strong body of scientific research that validates their use in education. As the technical development has increased, the availability of affordable simulators has increased their use in driver education. Unfortunately the research is not as strong as with the aviation or medical education. There are some support that simulator-based education can improve hazard perception but not so many other skills. Therefore I want to examine the effectiveness of a simulator in teaching Eco-driving skills to drivers. 20 students from Yrkesakademin were recruited as they were learning to drive trucks. The study is of between group design where the experimental group practiced Eco-driving skills in the simulator. Data were collected of the participants fuel consumption and speed. The control group were shown a video lecture on Eco-driving. The experimental group did significant improve while the control group did not. These results support the effectiveness of simulator-based education of Eco-driving skills. It also is encouraging for similar driving skills that can have a significant effect on traffic safety. While there is encouraging evidence for reducing the cost of driver education at the same time the students learning is enhanced.

Abstract The escalating fuel price in Taiwan has prompted the transportation industry to explore renewable energy sources for fuels, but what is more urgent at this stage is to improve transportation efficiency to cut transportation costs. The aim of this study is to implement fuel-efficient rewards to modify driving behavior, thereby improving fuel efficiency. The outcome of this strategy is not only about slowing down greenhouse gas production, but also a reduction of fuel costs of transportation companies. Every year highway bus companies consume millions of liters of fuel, and their fuel costs often exceed hundreds of millions of dollars. Therefore, how to conserve on fuel consumption has become an important issue for all of the bus companies. However so far, besides the eventual fuel savings data, there is still a lack of objective methods to evaluate the execution of such conservation programs. the project will use the ¡§Motivation-Opportunity-Ability Method¡¨ ¡]MOA¡^to develop an analysis model; verification of dynamic data analysis will be conducted using Single or Multilevel Structural Equation Modeling (Multilevel SEM). According to the above research results show that Taken together the investigators suggest those public as well as private transportation companies that have not implemented fuel-efficient related policies to start planning fuel-efficient reward programs and implementing the programs as soon as possible. Furthermore, transportation companies should give courses about environmental driving to teach drivers correct environmental-friendly driving as well as award or publicly praise fuel-efficient drivers. These strategies will bring good outcome for drivers, transportation companies, and environmental protection.

This master thesis deals with effects of green logistics onto logistics, which is closely connected to international trade. A part of this theses is also analysis of methods of green logistics used in company AB-InBev.

The objective of this thesis is to analyse driving behaviour and to characterize the effectsof an efficient way of driving, termed eco-driving, that enables the driver to reduce fuelconsumption and CO2emissions.The approach used to assess driving style is a collection of data from a CAN bus of acar equipped with OBD-II (on-board diagnostic) system. The driving experiment wasperformed for nine drivers who drove in a normal way or regular driving style and onedriver was an eco-driver who drove in an economical driving style. The driving routewas approximately 18.7 kms (which took between 25 to 30 minutes) in Halmstad city,Sweden.The drivers are compared using a statistical analysis of the driving parameters such as,speed, accelerator (gas pedal) and brake pressure, which are obtained from CAN busdata. A hierarchical clustering algorithm also used to classify the drivers based on theaverage result of the signals.In the results, a driving difference between the eco-driver and the normal drivers is visi-ble, most of the normal drivers have more or less similar behaviour. The average speed ofthe eco-driver lower than the normal drivers and the accelerator (gas pedal) result is alsoshown less usage by the eco-driver than the normal drivers. On the other hand, the eco-driver has braked more often than the normal drivers, but gently. Nevertheless, differenttraffic conditions during the experiment obstructs comparisons between the drivers.

<p>U radu su prikazani efekti teorijske i praktične obuke vozača, odnosno izvršena je analiza parametara vožnje u periodu pre obuke kao i u kratkoročnom i dugoročnom periodu nakon realizovane obuke. Na osnovu utvrđenih relacija između parametara, formiran je model za optimizaciju periodične obuke vozača. Primenom matematičke analize i statističkih testova u longitudinalnom periodu ispitivanja definisan je tačan period ponovne obuke vozača pri postizanju maksimalne dobiti za transportno preduzeće uvažavajući i proprtane troškove. Rezultati analize su pokazali da su pozitivni efekti eko-vožnje značajno izraženi u dobrim vozačkim uslovima. Loši vremenski uslovi koji uslovljavaju i loše uslove vožnje smanjuju pozitivne efekte obuke vozača sa povećanim zahtevom za bezbednijom vožnjom. Model bi mogao biti primenljiv za testiranje efekata obuke vozača u različitim transportnim kompanijma i različitim tržištima.</p> / <p>Тhis paper shows the effects of theoretical and practical eco-driving training, analysing driving parameters in the period before training, in the short-term and long-term after the training. A model was formed to optimize periodical driver training based on the established relationships between the parameters. Using the mathematical analysis and statistical tests in the longitudinal observation period, the exact period of the drivers retraining was defined in order to maximize profit for the transport company, taking into account the planned costs. The results indicate that the positive effects of eco-driving were significantly expressed in good driving conditions. With bad weather conditions that correlate with bad driving conditions, the effects of eco-driving training are suffocated with increased requirements for safer driving. The model could be applicable to test the effects of drivers training in different transportation companies and different markets.</p>

The development of connected vehicle (CV) technologies enables advanced management of individual vehicles and traffic signals to improve urban mobility and energy efficiency. In this thesis, a cooperative vehicle-signal control system will be developed to integrate an Eco-driving system and a proactive signal control system under a mixed connected environment with both connected vehicles (CVs) and human-driven vehicles (HDVs). The system utilizes CVs to conduct an accurate prediction of queue length and delay at different approaches of intersections. Then, a queue-based optimal control strategy is established to minimize the fuel usage of individual CVs and the travel time delay of entire intersections. The system applies the model predictive control to search for the optimal signal timing plan for each intersection and the most-fuel efficient speed profiles for each CV to gain the global optimum of the intersection. In this thesis, a simulation platform is designed to verify the effectiveness of the proposed system under different traffic scenarios. The comparison with the eco-driving only and signal control only algorithms verifies that the cooperative system has a much more extensive reduction range of the trip delay in the case of medium and high saturation. At low saturation, the effect of the system is not much different from that of the eco-driving algorithm, but it is still better than the signal control. Results show that the benefits of CVs are significant at all different market penetration rates of CVs. It also demonstrates the drawback of the system at high congestion levels. / Thesis / Master of Applied Science (MASc)

This thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world drivecycles and its performance is compared against global optimal results evaluated using DP. / Thesis / Master of Applied Science (MASc)

Fuel consumption of transportation vehicles account for nearly 27% of CO2 emissions from fossil fuel. The environmental impact of of transportation can be lowered with effective ECOfriendly driving. As modern vehicles are equipped with the on-board diagnostics (OBD) system that provides vehicle’s self-diagnostic data which is accessible to from the OBD-II socket in vehicles. The aim of this thesis is to design and implement such a monitor system that consists of an OBD-II reader (containing a Bluetooth communication module) to fetch data from the OBD-II socket and then, via a smartphone, send the data to a cloud server for analysis and visualisation. For this purpose, an ELM327 Bluetooth module (an off-the-shelf OBD-II dongle) was bought, an application has been created for an Android smartphone, and a cloud server has been created using ThinkSpeak (an online solution for cloud data collection and visualisation).   As a result, such monitor system has been implemented at low cost, and then tested and evaluated together with a Toyota Prius driving a round-trip between the Swedish cities of Uppsala and Arlanda. The test results have shown that the system is successful in gathering data from the OBD-II socket and then sending visualizing data. It needs, however, further work to increase the user-friendliness and accuracy of the gathered information from the vehicle. / OBD-II är en internationell standard i fordon som är obligatorisk i moderna bilar möjliggör att med hjälp av hårdvara läsa av information från bilen under färd. Det medför att man kan överföra data så som hastighet, bränsleförbrukning och motortemperaturfrån bilen till en annan tjänst där det presenteras för föraren. Metoden går ut på att transmittera informationen som läses av sensorer i bilen till en internettupkopplad mobiltelefon som för över informationen till en molntjänsst. Datan från bilen ska sedankompletteras med positionsinformation från mobilenhetens GPS enhet. Detta medför att skapandet av en applikation som kan uppfylla målen blir är en central del av projektet. ELM327 är en hårdvarumodul som är designad för att kopplas till ett OBD-II uttag. Modulen kan då läsa av data från CAN bussen i bilen och sedan konverterainformationen till en Bluetooth enhet. En mobiltelefon kan då kommunicera med Bluetooth enheten genom att skapa en Android applikation i utveklarverktyget Androidstudio. Applikationen för sedan över datan till molntjänsten ThingSpeak, som är designat för IoT project. ThingSpeak har en implementering av MatLab, en programmerings plattform för ingenjörer, som låter användaren analysera och visualiseradata i molntjänsten. En begränsning som uppstår är hur mycket pengar som investeras i projetet vilket har inflytande på hur ofta datan kan uppdateras i molntjänsten, vilket påverkar nogrannheten i resultatet. I tesen diskuteras även begränsningarna av systemet och olika utvecklingsmöjligheter av projektet.