Near-Lossless Compression for Large Traffic Networks

With advancements in sensor technologies, intelligent transportation systems can collect traffic data with high spatial and temporal resolution. However, the size of the networks combined with the huge volume of the data puts serious constraints on system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this paper, we analyze the reconstruction efficiency of several low-dimensional models for large and diverse networks. The compression performed by low-dimensional models is lossy in nature. To address this issue, we propose a near-lossless compression method for traffic data by applying the principle of lossy plus residual coding. To this end, we first develop a low-dimensional model of the network. We then apply Huffman coding (HC) in the residual layer. The resultant algorithm guarantees that the maximum reconstruction error will remain below a desired tolerance limit. For analysis, we consider a large and heterogeneous test network comprising of more than 18 000 road segments. The results show that the proposed method can efficiently compress data obtained from a large and diverse road network, while maintaining the upper bound on the reconstruction error.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program

Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted

Improved information flow topology for vehicle convoy control

A vehicle convoy is a string of inter-connected vehicles moving together for mutual support, minimizing traffic congestion, facilitating people safety, ensuring string stability and maximizing ride comfort. There exists a trade-off among the convoy's performance indices, which is inherent in any existing vehicle convoy. The use of unrealistic information flow topology (IFT) in vehicle convoy control, generally affects the overall performance of the convoy, due to the undesired changes in dynamic parameters (relative position, speed, acceleration and jerk) experienced by the following vehicle. This thesis proposes an improved information flow topology for vehicle convoy control. The improved topology is of the two-vehicle look-ahead and rear-vehicle control that aimed to cut-off the trade-off with a more robust control structure, which can handle constraints, wider range of control regions and provide acceptable performance simultaneously. The proposed improved topology has been designed in three sections. The first section explores the single vehicle's dynamic equations describing the derived internal and external disturbances modeled together as a unit. In the second section, the vehicle model is then integrated into the control strategy of the improved topology in order to improve the performance of the convoy to two look-ahead and rear. The changes in parameters of the improved convoy topology are compared through simulation with the most widely used conventional convoy topologies of one-vehicle look-ahead and that of the most human-driver like (the two-vehicle look-ahead) convoy topology. The results showed that the proposed convoy control topology has an improved performance with an increase in the intervehicular spacing by 19.45% and 18.20% reduction in acceleration by 20.28% and 15.17% reduction in jerk by 25.09% and 6.25% as against the one-look-ahead and twolook- ahead respectively. Finally, a model predictive control (MPC) system was designed and combined with the improved convoy topology to strictly control the following vehicle. The MPC serves the purpose of handling constraints, providing smoother and satisfactory responses and providing ride comfort with no trade-off in terms of performance or stability. The performance of the proposed MPC based improved convoy topology was then investigated via simulation and the results were compared with the previously improved convoy topology without MPC. The improved convoy topology with MPC provides safer inter-vehicular spacing by 13.86% refined the steady speed to maneuvering speed, provided reduction in acceleration by 32.11% and a huge achievement was recorded in reduction in jerk by 55.12% as against that without MPC. This shows that the MPC based improved convoy control topology gave enough spacing for any uncertain application of brake by the two look-ahead or further acceleration from the rear-vehicle. Similarly, manoeuvering speed was seen to ensure safety ahead and rear, ride comfort was achieved due to the low acceleration and jerk of the following vehicle. The controlling vehicle responded to changes, hence good handling was achieved

Route Planning Algorithms for Urban Environment

Master'sMASTER OF ENGINEERIN

Evolving Clustering Algorithms And Their Application For Condition Monitoring, Diagnostics, & Prognostics

Applications of Condition-Based Maintenance (CBM) technology requires effective yet generic data driven methods capable of carrying out diagnostics and prognostics tasks without detailed domain knowledge and human intervention. Improved system availability, operational safety, and enhanced logistics and supply chain performance could be achieved, with the widespread deployment of CBM, at a lower cost level. This dissertation focuses on the development of a Mutual Information based Recursive Gustafson-Kessel-Like (MIRGKL) clustering algorithm which operates recursively to identify underlying model structure and parameters from stream type data. Inspired by the Evolving Gustafson-Kessel-like Clustering (eGKL) algorithm, we applied the notion of mutual information to the well-known Mahalanobis distance as the governing similarity measure throughout. This is also a special case of the Kullback-Leibler (KL) Divergence where between-cluster shape information (governed by the determinant and trace of the covariance matrix) is omitted and is only applicable in the case of normally distributed data. In the cluster assignment and consolidation process, we proposed the use of the Chi-square statistic with the provision of having different probability thresholds. Due to the symmetry and boundedness property brought in by the mutual information formulation, we have shown with real-world data that the algorithm’s performance becomes less sensitive to the same range of probability thresholds which makes system tuning a simpler task in practice. As a result, improvement demonstrated by the proposed algorithm has implications in improving generic data driven methods for diagnostics, prognostics, generic function approximations and knowledge extractions for stream type of data. The work in this dissertation demonstrates MIRGKL’s effectiveness in clustering and knowledge representation and shows promising results in diagnostics and prognostics applications

Integration of body sensor networks and vehicular ad-hoc networks for traffic safety

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.Peer ReviewedPostprint (author's final draft

SHORT TERM TRAVEL BEHAVIOR PREDICTION THROUGH GPS AND LAND USE DATA

The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle’s destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. This dissertation has three main hypotheses; 1) Employing a tiered Markov model structure will permit a shorter learning period while achieving similar accuracy results, 2) The addition of derived trip purpose information will increase accuracy of the start of trip and in-route models as a whole, and 3) Similar methodologies of travel pattern inference can be used to accurately predict trip purpose and socio-economic factors. To study these concepts, a database of GPS driving traces (120 participants for 70 days) is collected. To model the user’s trip purpose, a new data source was explored: Point of Interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data, and travel patterns, trip purpose is calculated with machine learning methods. A new model structure is developed that uses trip purpose when it is available, yet falls back on traditional spatial temporal Markov models when it is not. The start of trip model has an overall increase of accuracy over other start of trip models of 2%. This comes quickly, needing only 30 days to reach this level of accuracy compared to nearly a year in many other models. When adding trip purpose and the start of trip model to in-route prediction methods, the accuracy of the destination prediction increases significantly: 15-30% improvement of accuracy over similar models between 0-50% of trip progression. Certain trips are predicted more accurately than others: work and home based trips average of 90% correct prediction, whereas shopping and social based trips hover around the 50% mark. In all, the greatest contribution of this dissertation is the trip purpose methodology addition and the tiered Markov model structure in gaining fast results in both the start of trip and in-route models

The impact of road and parking pricing on traffic congestion in the major shopping areas of regional cities

Over several decades, road authorities around the world have implemented road pricing to help reduce traffic congestion. Most implementations of pricing have been for Central Business Districts (CBD) of very large cities, which usually incorporate a range of retail and commercial activities. The studies have typically reported road pricing to be a successful means to reduce traffic congestion. Regional cities also experience traffic congestion in their CBD but the nature of the activities is different from large cities, in that shopping is a major activity. Raising the cost of road use and parking in a regional city CBD area would be expected to affect the number of trips made (trip generation), trip chaining patterns and mode of travel choice. The research problems were the congestion resulting from shopping trips to the CBDs of regional cities, and the lack of research on regional cities, shopping behavioural knowledge in the case of road pricing and increasing parking pricing, and the price elasticity of shopping demand in regional contexts. The research objectives were to understand the reasons for traffic congestion in the CBDs of regional cities, to identify the key variables governing shopping activity in regional cities, and to investigate the effect of road pricing and increasing parking pricing on shopping trip congestion in regional cities’ CBDs. This thesis presents an investigation into the impacts of the introduction of road charges and increasing parking fees on shopping trips in the Central Business Districts of Australian regional cities. The research methodology started with choosing a typical regional city. The regional city of Toowoomba, Queensland. Australia was selected as a representative case study and demographic data obtained. A survey was designed to examine the predominant variables in shopping activity and to source views on any impact on consumer behaviour by the introduction of road pricing and increasing parking pricing. The survey instruments were LimeSurvey tool and hardcopy questionnaires. The completed obtained responses were 304 responses. Survey data was also used to construct a Toowoomba shopping trips numerical predictive model. The four-step traditional method was used to construct the model. The model was validated by comparing the observed shopping trips to the shopping centres with the predicted shopping trips resulting from the model. The model was designed and constructed to predict the impact of changes in road pricing and increased parking pricing on shopping trips. Lastly, the model was applied for transferring the study results to other Australian regional cities. Transferring the results of the model depended on the similarity of the characteristics between the other regional cities and the case study city. The main shopping habits in the selected regional city were that shopping locally was preferred, that shopping within the CBD was more often undertaken by females than males for all four directions of travel, and that travelling from home to shopping centres in the CBD was by car. So, the study helped in understanding shopping habits, the governing variables, and the distribution of shopping trips. These were the first and second objectives of the research, and answered the first and second research questions of the study. In regard to the third objective of the research and answering the third and fourth research questions, the study demonstrated that introducing road pricing and increasing parking pricing were techniques that could be applied to assist in managing traffic congestion demand in the CBDs of regional cities. The results for increased pricing indicated a reduction in shopping trips to the CBD as a result of reducing frequency of shopping in the CBD, changing of shopping destinations, and moving towards increased use of public transport to the CBD. The effect of introduction road pricing on shopping trips was that 57% of the CBD shoppers would shop less at the CBD. Similarly, 55% of respondent's indicated increasing parking pricing by $4 per hour would significantly affect their shopping in the CBD. It was also noted that about 25$4) were introduced. The results of the research study would be applicable in other Australian regional cities with similar demographics