Predicting vehicular travel times by modeling heterogeneous influences between arterial roads

Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of travel time prediction in arterial roads using data sampled from probe vehicles. There is only a limited literature on methods using data input from probe vehicles. The spatio-temporal dependencies captured by existing data driven approaches are either too detailed or very simplistic. We strike a balance of the existing data driven approaches to account for varying degrees of influence a given road may experience from its neighbors, while controlling the number of parameters to be learnt. Specifically, we use a NoisyOR conditional probability distribution (CPD) in conjunction with a dynamic bayesian network (DBN) to model state transitions of various roads. We propose an efficient algorithm to learn model parameters. We propose an algorithm for predicting travel times on trips of arbitrary durations. Using synthetic and real world data traces we demonstrate the superior performance of the proposed method under different traffic conditions.Comment: 13 pages, conferenc

The effect of GPS refresh rate on measuring police patrol in micro-places

© 2021, The Author(s). With the increasing prevalence of police interventions implemented in micro hot-spots of crime, the accuracy with which officer foot patrols can be measured is increasingly important for the robust evaluation of such strategies. However, it is currently unknown how the accuracy of GPS traces impact upon our understanding of where officers are at a given time and how this varies for different GPS refresh rates. Most existing studies that use GPS data fail to acknowledge this. This study uses GPS data from police officer radios and ground truth data to estimate how accurate GPS data are for different GPS refresh rates. The similarity of the assumed paths are quantitatively evaluated and the analysis shows that different refresh rates lead to diverging estimations of where officers have patrolled. These results have significant implications for the measurement of police patrols in micro-places and evaluations of micro-place based interventions

FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For this purpose, this paper proposes a novel time-based system, namely FraudMove, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed FraudMove system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. FraudMove employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows FraudMove to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, FraudMove discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of FraudMove in detecting outlier trajectories. The experimental results prove that FraudMove saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems

Analysis of Berlin's taxi services by exploring GPS traces

With current on-board GPS devices a lot of data is being collected while operating taxis. This paper focuses on analysing travel behaviour and vehicle supply of the Berlin taxi market using floating car data (FCD) for one week each in 2013 and 2014. The data suggests that there is generally a demand peak on workday mornings and a second peak over a longer time in the afternoon. On weekends, the demand peaks shift towards the night. On the supply side, drivers seem to adapt to the demand peaks very efficiently, with fewer taxis being available at times of low demand, such as during midday. A spatial analysis shows that most taxi trips take place either within the city centre or from/to Tegel Airport, the city's largest single origin and destination. Drivers spend a large amount of their work time on waiting for customers and the taxi rank at Tegel Airport is the most popular one

Nonlinear state space smoothing using the conditional particle filter

To estimate the smoothing distribution in a nonlinear state space model, we apply the conditional particle filter with ancestor sampling. This gives an iterative algorithm in a Markov chain Monte Carlo fashion, with asymptotic convergence results. The computational complexity is analyzed, and our proposed algorithm is successfully applied to the challenging problem of sensor fusion between ultra-wideband and accelerometer/gyroscope measurements for indoor positioning. It appears to be a competitive alternative to existing nonlinear smoothing algorithms, in particular the forward filtering-backward simulation smoother.Comment: Accepted for the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 201

Understanding the deterrent effect of police patrol

The fact that crime clusters spatially has been known since at least the early 19th century. However, understanding of the extent and nature of this clustering at different areal units, and the fact that crime also clusters at different temporal scales is relatively new. Where previously the most at-risk areas (or `hot-spots') of crime were defined over areas the size of city districts and for periods of months if not years, the last decade has seen the focus shift to micro-places - areas of only a few hundred metres across - which are only `hot' for days or even hours. The notion that visible police presence in crime hot-spots can deter crime is not new and has been the basis of police patrols for two centuries. This deterrent effect has been well evidenced in many previous studies, both by academics and police practitioners. However, evaluations of these more recent micro-level hot-spot patrol strategies face significant analytic challenges and data quality concerns. They also often assume levels of police activity at the micro-area level (an `intention-to-treat' design) rather than measuring it directly. The aim of this thesis is to investigate the accuracy and precision of data that can be used to evaluate micro-level hot-spot patrol strategies and the implications this has for any analysis conducted using such data at these micro-level geographies. This thesis begins by outlining the relevant literature regarding place-based policing strategies and the current understanding of how crime clusters in both space and time. It continues by highlighting the data challenges associated with evaluating micro-level police interventions through the use of an illustrative analytic strategy before using a self-exciting point process model to evaluate the effects of police foot patrol in micro-level hot-spot under the assumption that the crime and patrol data being used are accurate. This is followed by two chapters which investigate the quality of the two datasets. Finally, the point-process evaluation is re-conducted using simulated data that takes account of the uncertainty of the datasets to demonstrate how data quality issues effect the result of such an evaluation and ultimately, the perceived efficacy of these highly-focussed policing strategies