Multi-signal Anomaly Detection for Real-Time Embedded Systems

This thesis presents MuSADET, an anomaly detection framework targeting timing anomalies found in event traces from real-time embedded systems. The method leverages stationary event generators, signal processing, and distance metrics to classify inter-arrival time sequences as normal/anomalous. Experimental evaluation of traces collected from two real-time embedded systems provides empirical evidence of MuSADET’s anomaly detection performance. MuSADET is appropriate for embedded systems, where many event generators are intrinsically recurrent and generate stationary sequences of timestamp. To find timinganomalies, MuSADET compares the frequency domain features of an unknown trace to a normal model trained from well-behaved executions of the system. Each signal in the analysis trace receives a normal/anomalous score, which can help engineers isolate the source of the anomaly. Empirical evidence of anomaly detection performed on traces collected from an industrygrade hexacopter and the Controller Area Network (CAN) bus deployed in a real vehicle demonstrates the feasibility of the proposed method. In all case studies, anomaly detection did not require an anomaly model while achieving high detection rates. For some of the studied scenarios, the true positive detection rate goes above 99 %, with false-positive rates below one %. The visualization of classification scores shows that some timing anomalies can propagate to multiple signals within the system. Comparison to the similar method, Signal Processing for Trace Analysis (SiPTA), indicates that MuSADET is superior in detection performance and provides complementary information that can help link anomalies to the process where they occurred

Timely Data Delivery in a Realistic Bus Network

Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I

17-11 Evaluation of Transit Priority Treatments in Tennessee

Many big cities are progressively implementing transit friendly corridors especially in urban areas where traffic may be increasing at an alarming rate. Over the years, Transit Signal Priority (TSP) has proven to be very effective in creating transit friendly corridors with its ability to improve transit vehicle travel time, serviceability and reliability. TSP as part of Transit Oriented Development (TOD) is associated with great benefits to community liveability including less environmental impacts, reduced traffic congestions, fewer vehicular accidents and shorter travel times among others.This research have therefore analysed the impact of TSP on bus travel times, late bus recovery at bus stop level, delay (on mainline and side street) and Level of Service (LOS) at intersection level on selected corridors and intersections in Nashville Tennessee; to solve the problem of transit vehicle delay as a result of high traffic congestion in Nashville metropolitan areas. This study also developed a flow-delay model to predict delay per vehicle for a lane group under interrupted flow conditions and compared some measure of effectiveness (MOE) before and after TSP. Unconditional green extension and red truncation active priority strategies were developed via Vehicle Actuated Programming (VAP) language which was tied to VISSIM signal controller to execute priority for transit vehicles approaching the traffic signal at 75m away from the stop line. The findings from this study indicated that TSP will recover bus lateness at bus stops 25.21% to 43.1% on the average, improve bus travel time by 5.1% to 10%, increase side street delay by 15.9%, and favour other vehicles using the priority approach by 5.8% and 11.6% in travel time and delay reduction respectively. Findings also indicated that TSP may not affect LOS under low to medium traffic condition but LOS may increase under high traffic condition

Costs of Interchange: A Review of the Literature.

Interchange within mode influences the demand for that mode through the effect it has on time spent waiting, time spent transferring between vehicles and the inconvenience and risks involved, whilst interchange between modes has additional implications in terms of information provision, through ticketing and co-ordination. The valuation and behavioural impact of each of these factors will vary with an individual’s socio-economic and trip characteristics as well as with the precise features of the interchange. A reduction in the costs of interchange brought about by an improvement to any of the above factors will lead to increasingly ‘seamless journeys’ and such benefits which must be quantified. Indeed, this issue has been identified as an area of key importance in the Government’s Transport White Paper (DETR, 1998a) which states: Quick and easy interchange is essential to compete with the convenience of car use. This message was reiterated by the draft guidance for Local Transport Plans (DETR, 1998b), which called for: more through-ticketing, better connections and co-ordination of services, wider availability of information and improved waiting facilities. Rather than being perceived simply as a barrier to travel, quality interchange is now also being regarded as an opportunity to create new journey opportunities. A recent report on the subject of interchange (Colin Buchanan and Partners, 1998) claimed that : It will become more sensible and economic to base public transport networks around the concept of interchange rather than the alternative of trying to avoid it. whilst in response to the diffuse travel patterns made possible by increased car availability, CIT (1998) commented: people should readily be able to complete a myriad of journeys by changing services (and modes) if a through facility is not available. Ease of interchange should be something we take for granted. Regardless of the precise direction in which transport policy and public transport provision develop, practical constraints and the fact that the most heavily trafficked routes tend to have through services places limitations on the extent to which the need to interchange can be reduced whilst no matter how fully integrated different modes of transport are the need to transfer between them cannot be removed. In contrast, the need to change would inevitably increase with the adoption of a practice of building networks around interchange to create new journey opportunities. However, there is considerable scope to improve existing interchange situations or to design new ones which impose minimum costs. Although previous empirical research has focused on the need to interchange or not, and this remains important, it is essential that research is also directed at improvements which facilitate interchange.The aims of this study, as set out in the terms of reference, are centred around the demand side response to interchange rather than the technical supply side issues relating to improving interchange and integration which have been covered in other studies (Colin Buchanan and Partners, 1998; CIT, 1998). The objectives are: to explore the extent to which the reality and perception of interchange deters public transport use, absolutely and in relation to other deterrents to investigate how public transport users perceive interchange; how they make choices and trade-offs in travel cost and time and the influence of interchange attributes (e.g. information, through ticketing) on those choices to assess which components of interchange act as the greatest deterrent to travel to investigate the extent to which interchange penalties vary according to journey purpose, distance and time of travel (or other factors)

Capturing in-situ Feelings and Experiences of Public Transit Riders Using Smartphones

High-density urban environments are susceptible to ever-growing traffic congestion issues, which speaks to the importance of implementing and maintaining effective and sustainable transportation networks. While transit oriented developments offer the potential to help mitigate traffic congestion issues, transit networks ought to be safe and reliable for ideal transit-user communities. As such, it is imperative to capture meaningful data regarding transit experiences, and deduce how transit networks can be enhanced or modified to continually maintain ideal transit experiences. Historically speaking, it has been relatively tricky to measure how people feel whilst using public transportation, without leaning on recall memory to explain such phenomena. Recall memory can be vague and is often less detailed than recording in-situ observations of the transit-user community. This thesis explores the feasibility of using smartphones to capture meaningful in-situ data to leverage the benefits of the Experience Sampling Method (ESM), while also addressing some limitations. Students travelled along Grand River Transit bus routes in Waterloo, Ontario from Wilfrid Laurier University to Conestoga Mall and back using alternate routes. The mobile survey captured qualitative and quantitative data from 145 students to explore variations in wellbeing, and the extent to which environmental variables can influence transit experiences. There were many findings to consider for future research, especially the overall role anxiety played on transit experiences. In addition, the results indicate that the methodology is appropriate for further research, and can be applied to a wide range of research topics. In particular, it is recommended that a similar study be applied to a much larger, and more representative sample of the transit-user community. Future considerations are discussed as key considerations to leverage the benefits of ESM research, and the promise it can bring towards the enhancement of transit experiences and the cohesion of transit-user communities

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers