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Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data
Information Fusion 12 (2011) 20–27
Contents lists available at ScienceDirect
Information Fusion journal homepage: www.elsevier.com/locate/inffus
Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data Hussein Dia a,*, Kim Thomas b,1 a b
School of Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia Parsons Brinckerhoff, 69 Ann Street, Brisbane, Queensland 4001, Australia
a r t i c l e
i n f o
Article history: Received 10 August 2007 Received in revised form 24 June 2009 Accepted 7 January 2010 Available online 18 January 2010 Keywords: Automatic incident detection Neural networks Data fusion Microscopic traffic simulation
a b s t r a c t This paper describes the development of neural network models for automatic incident detection on arterial roads, using simulated data derived from inductive loop detectors and probe vehicles. The work reported in this paper extends previous research by comparing the performance of various data fusion neural network architectures and assessing model performance for various probe vehicle penetration rates and loop detector configurations. Data from 108 incidents was collected from loop detectors and probe vehicles using a calibrated and validated traffic simulation model. The best performance was obtained for detector configurations found on most existing road networks, with a detection rate of 86%, false alarm rate of 0.36% and probe vehicle penetration rate of 20%. Fusion of speed data further improved performance, resulting in an incident detection rate of 90% and a false alarm rate of 0.5%. The results reported in this paper demonstrate the feasibility of developing advanced data fusion neural network architectures for detection of incidents on urban arterials using data from existing loop detector configurations and probe vehicles. Ó 2010 Elsevier B.V. All rights reserved.
1. Introduction The high cost of congestion caused by incidents, mainly in terms of traffic delays, air pollution and deteriorated safety conditions, has prompted a growing worldwide interest in developing efficient and effective automated incident detection methods. Incidents are defined as non-recurring events such as accidents, disabled vehicles, spilled loads, maintenance work and other events that disrupt normal traffic flow and result in a capacity reduction of a facility. Such incidents account for a large percentage of the total delays on major road facilities around the world. For example, it has been reported that for every minute an incident remains un-cleared after its occurrence, it takes around four minutes for traffic to recover [17]. The benefits to be derived from early incident detection and quick response can drastically reduce traffic delays and improve road safety and real-time traffic control. Motorists can be informed by providing real time traveler information to allow for alternate routing of traffic and timely dispatch of emergency services. Intelligent transportation systems (ITS) technologies are structured to
* Corresponding author. Present address: Aurecon Australia Pty. Ltd., 32 Turbot Street, Brisbane Queensland 4001, Australia. Tel.: +61 7 3173 8670; fax: +61 7 3173 8001. E-mail addresses: [email protected] (H. Dia), [email protected] (K. Thomas). 1 Tel.: +61 7 3854 6200; fax: +61 7 3854 6500. 1566-2535/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.inffus.2010.01.001
address these needs through advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). For these systems to be effective, it is necessary to develop procedures for detecting incidents which are both reliable and quick to respond. Automated incident detection (AID) is essentially a pattern classification problem, where traffic data is analyzed and classified into one of two categories (an incident or non-incident pattern). A number of automatic incident detection algorithms have been developed or proposed over the last two decades for both the freeway and urban arterial road environments. The structure of these algorithms varies in the degree of sophistication, complexity and data requirements. Inductive loop detectors embedded in the road pavement are typically used to obtain the traffic data needed for these techniques. The loops provide volume (vehicles per time interval); occupancy (percent of time the loop is occupied by a vehicle) and in cases where dual loops are installed (such as on freeways but not necessarily arterials), speeds (kilometer per hour) are also obtained. Incident detection models based on time series analysis [2]; pattern comparison [14,13]; catastrophe theory [15]; probabilistic networks [10]; flow modelling of state variables [4]; wavelet techniques [20]; fractal dimension theory [23]; support vector machines [25]; artificial neural networks [16,3,9, 6,1,10]; Bayesian models [26] and fuzzy logic [8] were attempted for both the freeway and arterial environments and have been shown to exhibit varying levels of detection performance. Several
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other approaches, including video image processing, have also been developed and demonstrated. Each of these systems has its advantages and limitations with respect to cost, operational performance and area coverage. For the detection of lane-blocking incidents, the literature suggests that traffic data from inductive loop detectors feeding into an artificial neural network offers the highest detection rate and lowest false alarm rate [25,6]. The same literature, however, indicates that none of the techniques developed so far meets the operational needs of traffic management and control center operators, who demand a much lower false alarm rate to minimize operator overload. To overcome this problem, a neural network data fusion technique is proposed in this study to integrate traffic data from loop detectors with travel time data collected from probe vehicles. Probe vehicles can be used to collect time stamped locations and speeds. Buses, taxis and other commercial vehicles are commonly used for this purpose. Vehicles equipped with tags for electronic toll collection can also be used, provided the necessary infrastructure is available. The proportion of probe vehicles on the road network (referred to as penetration rate in this paper) plays an important role in the reliability of the probe vehicle data and its use for incident detection. The focus of this paper will be on incident detection on urban arterial roads, which are a more challenging environment than freeways due to the following factors:
j Freeways have limited access points whereas arterials have multiple access points. j Left and right turn movements at intersections make traffic movements more unpredictable. j Intersection control results in variable and periodic interruptions of flow, queuing of vehicles at intersections, and dispersion of vehicle platoons downstream. j Arterials usually operate at lower speeds which make lane changes easier, hiding the effects of an incident. j Arterials typically have more limited surveillance infrastructure than freeways. A number of studies have attempted the arterial incident detection problem (e.g. [7,21,19,18,9,11,12,25]). This study extends previous research by developing and testing various neural network data fusion architectures based on simulated loop detector and probe vehicle data for varying penetration rates [22]. Due to the difficulty of collecting field incident data, this study will rely on data generated from a microscopic traffic simulator capable of re-producing the impacts of lane-blocking incidents. This study also considers the impacts of different detector configurations. Two neural network architectures will be used: multi-layer feedforward (MLF) networks and modular networks. MLF networks consist of an input layer, linked by weighted connections to one or more hidden layers, which are in turn linked by weighted connections to the output layer. The hidden layers allow the network to learn the nonlinear relationships between the inputs and outputs. Modular networks consist of two or more modules or experts (Fig. 1), typically MLF networks, which operate independently but receive the same input data. Depending on the architecture used, an additional network called the gating network may control the degree to which each module’s output affects the final network output, giving greater priority to the best performing module. Despite their apparent complexity, modular networks can learn faster, by breaking a task down into smaller sub-tasks. Also, the
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performance of a single module does not affect the performance of the others. This is a particular advantage in real-world situations, where data may sometimes be unavailable. 2. Automatic incident detection systems Automatic incident detection (AID) is an integral part of incident management systems. AID systems involve two main components: a traffic detection system and an incident detection algorithm. The traffic detection system provides the traffic information necessary for detecting an incident while the incident detection algorithm interprets that information and ascertains the presence or absence of incidents. As was reported before, inductive loop detectors embedded in the pavement are typically used to obtain traffic data. The data comprises speed, flow and occupancy and is typically provided in 20-s cycles. Data of this type would form the input to an incident detection algorithm which would raise an alarm to indicate the presence of an incident on the facility. Part of the challenge in developing automated incident detection algorithms is to deal with the inherent ‘noisy’ character of the raw input data and to be able to differentiate recurrent from non-recurrent congestion. 3. Performance evaluation criteria For this research, incidents are defined as lane-blocking events that cause a reduction in the capacity of the road. When evaluating the performance of an incident detection algorithm, the following measures or evaluation criteria would ideally be computed for an independent data set of incidents that were not used in the development or calibration of the algorithm. 3.1. Detection rate (DR) This is defined as the number of detected incidents divided by the total number of incidents that occurred during the recorded time. Detection rates greater than 90% are desirable. An additional requirement is that the incident must be detected within a short time (e.g. 5 min) of the start of the incident. If the incident is not detected within that timeframe, it will be deemed undetected.
DR ¼
No: of detected incidents 100% Total no: of recorded incidents
ð1Þ
3.2. False alarm rate (FAR) If the incident detection algorithm raises an incident alarm, while in fact no incident was present, then this constitutes a false alarm. The false alarm rate (FAR) can be defined in two different ways, depending on whether it is computed as an on-line or offline false alarm rate. When computing the false alarm rate during on-line testing, the on-line FAR is defined as:
FARon ¼
No: of detection intervals which gave false alarms Total no: of intervals over which the model was applied 100% ð2Þ
When testing the algorithm performance based on historical data (i.e. off-line testing), the onset and termination of incidents can usually be determined. This allows for the determination of the start and end times of incidents and consequently for the computation of the number of times the algorithm was applied during non-incident conditions. Therefore, the off-line false alarm rate is defined as:
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Fig. 1. Representation of a typical modular neural network architecture.
FARoff ¼
No: of detection intervals which gave false alarms Total no: of non-incident intervals in the entire data set 100% ð3Þ
To illustrate the difference between the on-line and off-line FAR, consider an algorithm that was tested for 100 intervals during which 10 intervals experienced a real incident and 15 other intervals experienced false alarms. The FARon would be (15%), and FARoff would be (15/90 or 16.7%). 3.3. Mean time-to-detect (MTTD) The time-to-detect (TTD) an incident is defined as the difference between the time the incident actually occurred (tio) and the time it was detected by the algorithm (tia). These times are usually not known precisely but estimates can be deduced from loop detector data or records kept by traffic control centers’. When evaluating the performance of the algorithm on multiple incidents, it is customary to report the average or mean time-to-detect (MTTD) of a set of (n) incidents. Therefore, the mean time-to-detection is defined as:
MTTD ¼
n 1X ðt ia t io Þ n i¼1
ð4Þ
3.4. Performance envelope curve area (PECA) The performance of incident detection algorithms in terms of DR, FAR and MTTD can vary depending on the selection of appropriate decision thresholds (DT) associated with each algorithm. The values selected for the decision thresholds play an important role in the classification of the input data and consequently in determining the incident detection performance of the model. The plot of DR against FAR (shown in Fig. 2) is called the performance envelope curve (PEC) of the model. At ideal performance, the DR would be 100% and the FAR would be zero. The quality of performance of the algorithm is demonstrated by the degree to which the PEC pushes upward and to the left as the DT values associated with the algorithm are relaxed. This can be quantified by the area under the curve (PECA) and the slope of the curve (particularly for low values of FAR). The PECA for a perfect discriminator will be 10,000 (i.e. 100 100). This procedure is particularly useful since it helps with evaluating the model’s performance based on the total representation of DRs, FARs and MTTD by using a single index, i.e. the area under the PEC.
The above definitions clearly show that both the DR and FAR measure the effectiveness of the algorithm while the MTTD reflects its efficiency. It can also be deduced that DR and FAR are positively correlated. In order to detect more incidents, the algorithm thresholds are relaxed and that causes some incident-free intervals to be interpreted as alarms. Since many false alarms are caused by random fluctuations in traffic flow, a persistence test is usually applied by testing multiple incident warnings in a few consecutive intervals before declaring an alarm. This method, however, reduces the efficiency of the algorithm because it results in an increase in the MTTD. Clearly, all the measures are all inter-related. The relative importance of the measures, however, is typically DR, FAR and MTTD [16]. When evaluating the effectiveness of AID algorithms under different conditions, it is common to compare their FAR for a fixed value of DR. Similarly, the efficiency of AID algorithms under different conditions can be evaluated by comparing their MTTD for fixed values of DR and FAR. 4. Model development Due to the difficulty of collecting field incident data, a traffic simulation model was used to generate the required loop detector and probe vehicle data. The main advantage of this approach is that once a model has been developed and validated, it is possible to generate data for a wide range of situations. For development of models of sufficient generality, a variety of incident conditions were required, with varying probe vehicle penetration rates. Data was collected using the PARAMICS traffic simulation tool. A network including two major arterials in Brisbane, including Coronation Drive and Milton Road (Fig. 3), was developed and validated [5,24]. Two detector configurations were simulated in this research (Fig. 4). Configuration 1 attempts to treat arterial links like freeways, and makes use of detectors installed downstream of intersections. This type of configuration is not currently available on most arterial roads. Configuration 2, on the other hand, is the standard configuration on arterial roads where loop detectors are located both upstream and downstream of intersections. For incident detection purposes, the section of road under surveillance (section of interest in Fig. 4) is typically bounded by the upstream (US) and downstream (DS) locations of the loop detectors. Vehicle identification units (VIDs), used to detect the probe vehicles, were modelled in the same locations as the loop detectors. To develop a standard input vector for configuration 2, detector site measurements were apportioned to the left turn, through and right turn movements at the upstream intersection.
H. Dia, K. Thomas / Information Fusion 12 (2011) 20–27
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Fig. 2. A performance envelope curve.
Fig. 3. Schematic of the testbed network.
Data was generated for a wide range of situations. Simulation model variables included: link length; incident location on link (upstream, midstream or downstream); incident duration (15, 30 or 60 min); incident severity (number of blocked lanes); and flow (>600 vph per lane or <600 vph per lane). Varying these features resulted in a set of 108 incidents for each configuration. Data was collected for each time step, irrespective of the traffic signal phase. A sample of the data is provided in Table 1 below. The data in this table is recorded in 20-s intervals and shows the speed (kph), flow (number of vehicles) and occupancy (percent of time the loop is occupied) for both the upstream and downstream detector stations. The last column in the table provides the incident conditions that were prevailing during that 20-s interval
within the section. A value of zero indicates incident-free conditions and a value of unity indicates incident conditions. The data collected from the simulator included a large number of samples which took into consideration the following parameters to ensure statistical reliability of data and the associated results. 4.1. Incident location The incident location within the link is an important parameter. If an incident occurs close to the downstream detector, it will take a while before the traffic close to the upstream detector is affected, so the performance of a detection system varies with the location of the incident relative to the detectors. Incidents were
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Fig. 4. Modeled detector configurations.
modelled at three points on the link in question: upstream, midstream and downstream, between the US and DS detectors. 4.2. Incident duration The longer an incident lasts, the greater its effect on the traffic during and after the incident. Longer incidents are expected to have greater false alarm rates because traffic will still be affected once the incident ends. Three incident durations were modelled: 15 min, 30 min and 60 min. Each incident was set to occur thirty minutes after the beginning of the simulation. During the first 15 min of the simulation, the traffic on the network to builds up to the required levels. 4.3. Incident severity It is expected that the more severe the incident, the greater effect on the traffic, and the easier it should be to detect. Incident severity was modelled by locating incidents on: Lane 1: Closest to the kerb for slow traffic – low severity. Lane 2: Closest to the median for faster traffic – medium severity. Both Lanes 1 and 2: Totally blocking the road – high severity.
4.4. Traffic flow The greater the traffic flow on a link, the more measurable the effect of an incident on the traffic. Two traffic flow regimes were modelled to simulate peak and off-peak traffic. In simulations, the traffic flow is determined by a combination of a configuration
variable, the demand weight and the origin/destination matrix. Low simulated flows were less than 600 vehicles per hour (vph), while high flows were greater than 600 vph. In total, 108 incidents were simulated for detector configurations 1 and 2: two link lengths were modelled under high and low flow conditions. Incidents were simulated at upstream, midstream and downstream positions on each link, occurring in the slow lane; the fast lane and both lanes, for durations of 15, 30 and 60 min. The output files from each simulation were then merged into a single file, containing loop measurements and probe vehicle travel times. Data was aggregated to 20 s intervals, in keeping with freeway incident detection algorithms [6]. While speed measurements were collected from the simulated loops, they were not used as input, as most arterial loop detectors would not have speed data available. Instead flow divided by occupancy was used as a proxy for speed. With the exception of calculation of the flow/occupancy ratios, no input data was pre-processed. The 108 data sets were then randomly allocated to the training, cross validation and validation data sets. The training set comprised 50% of the observations and was used to determine the parameters of the model. The Cross Validation data set comprised 10% of the observations and was used to prevent overtraining of the model. Finally, the validation data set comprised 40% of the observations and was used to evaluate the performance of the trained model. Therefore, the validation data set comprised observations which were not used in model training or calibration. Identical allocations were made for configurations 1 and 2, to facilitate comparison of results. The NeuroSolutions software package was then used to develop and train a series of MLF and modular networks. Two types of MLF architectures were trained: generalized feed-forward (GFF) networks which have connections that jump over layers for speedier
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H. Dia, K. Thomas / Information Fusion 12 (2011) 20–27 Table 1 Sample simulation data. Time stamp (20-s)
Speed upstream (kph)
Flow upstream (veh)
Occupancy upstream (%)
Speed downstream (kph)
Flow downstream (veh)
Occupancy downstream (%)
Incident conditions (binary)
00:00 00:20 00:40 01:00 01:20 01:40 02:00 02:20 02:40 03:00 03:20 03:40 04:00 04:20 04:40 05:00 05:20 05:40 06:00 06:20 06:40 07:00 07:20 07:40 08:00 08:20 08:40 09:00 09:20 09:40 10:00
26.6 43.5 43.3 42.7 45.5 23.3 57.2 58.9 23.7 0 43.6 40.0 39.3 16.2 41.1 57.1 34.9 46.9 44.4 41.5 61.2 14.4 39.7 15.6 42.0 38.6 38.6 0 19.0 60.4 22.9
4 8 2 6 6 13 15 10 12 0 6 8 9 2 10 7 4 21 3 8 11 6 9 7 3 10 2 0 1 1 2
7.2 7.5 3.5 5.1 9.1 40.5 14.2 6.0 16.0 66.6 3.5 5.5 8.5 2.3 6.7 5.2 4.2 20.5 2.8 6.3 9.3 23.6 8.7 20.2 3.6 7.0 2.4 66.6 1.2 2.1 0.5
54.1 61.2 54.1 61.5 45.6 32.1 45.8 31.3 26.7 0 58.7 64.0 57.7 41.9 45.7 0 55.8 0 49.5 46.3 29.2 27.7 0 65.4 45.5 0 51.9 0 0 32.3 51.8
9 4 2 5 9 5 3 4 5 0 9 5 5 6 6 0 5 0 1 5 10 7 0 3 8 0 6 0 0 6 4
5.6 5.3 3.2 7.8 11.2 16.2 6.7 6.0 29.1 0 8.1 4.9 5.4 8.3 13.1 0 4.9 0 4.5 8.1 30.0 10.6 0 6.2 13.2 0 8.0 0 0 6.6 4.4
0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0
learning; and Jordan/Elman (JE) networks which have additional processing elements (PEs), called context units, that remember past activity using a configurable exponentially decaying recency gradient. In addition, two types of modular networks architectures were also trained: MOD1 networks which contained two-expert modules; and MOD2 networks which comprised four-expert modules and an integrator axon to provide memory of inputs. A large number of neural networks were trained, for various numbers of PEs in the hidden layers. Once trained, the performance of each network on the validation data set was determined. It was found that the best performance was obtained using upstream and downstream occupancy, flow and flow/occupancy ratio, supplemented with travel times for varying probe vehicle penetration rates. In general, performance improved with increased probe vehicle penetration rate. 5. Results 5.1. Detector configuration 1 A series of MLF and modular neural networks were developed for various probe vehicle penetration rates and hidden layer structures. The best performances for each neural network architecture are provided in Table 2 below. All mean times to detect were well below 3 min.
Best results were obtained in all cases for a probe vehicle penetration rate of 20%. The best detection rate of 59% for a false alarm rate of 0.5% was obtained using a Jordan/Elman MLF network, which allowed input of data from previous time steps. The best performance for the modular neural networks was obtained with the more complex MOD2 network, with a detection rate of 49%. Overall, and as can be deduced from Table 2, the results for detector configuration 1 were not promising. 5.2. Detector configuration 2 A series of MLF and modular neural networks were also developed for various probe vehicle penetration rates and hidden layer sizes for detector configuration 2. The best performances for each neural network architecture are provided in Table 3 below. All mean times to detect were also well below 3 min. The best results were obtained using a Jordan/Elman MLF neural network (DR 86% and FAR 0.36%). These results were obtained with a probe vehicle penetration rate of 20%. The PECAs were again reasonably high. The best performance for the modular neural networks was obtained with a probe vehicle penetration rate of 5%, with a detection rate of 77%. This result was again achieved with the more complex MOD2 network. The model performed substantially better with the inclusion of probe vehicle data. For the best performing JE network, there was only a marginal
Table 2 Best results for detector configuration 1. Neural network architecture
Persistence test steps
Time constant
Best DR (%)
PECA
Penetration rate (%)
Generalized feed-forward (GFF) Jordan/Elman (JE) Modular (MOD1) Modular (MOD2)
0 0 0 0
– 0.5 – 0.5
40 59 49 49
9476 9667 9715 9508
20 20 20 20
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Table 3 Best detector configuration 2 results. Neural network architecture
Persistence test steps
Time constant
Best DR (%)
PECA
Penetration rate (%)
Generalized feed-forward (GFF) Jordan/Elman (JE) Modular (MOD1) Modular (MOD2)
0 1 0 0
– 0.8 – 0.8
54 86 35 77
9298 9697 9041 9732
0 20 0 5
Table 4 Detector configurations 1 and 2 performances for simulation model variables. Variable
Subtype
Configuration 1
Configuration 2
DR (%)
PECA
DR (%)
PECA
Link length
Coronation drive (long) Milton Rd (short)
82.0 38.0
9961 9527
96 76
9969 9531
Incident location on link
Upstream Midstream Downstream
53.5 65.5 48.0
9522 9692 9810
87 86 78
9555 9849 9586
Incident duration
15 min 30 min 60 min
77.0 44.5 53.0
9905 9529 9592
83 88 85
9790 9704 9622
Incident severity (lanes blocked)
Slow lane Fast lane Both lanes
77.0 69.0 8.5
9745 9732 9632
100 72 52
9998 9192 9936
Traffic flow
High (>600 vph/lane) Low (<600 vph/lane)
55.0 44.0
9347 9920
77 95
9199 9963
59.0
9667
86
9697
Overall performance
improvement in performance with increased probe vehicle penetration rate, demonstrating that, while desirable, probe vehicle data was not essential for that particular model. The results for detector configuration 2 are promising, and demonstrate the feasibility of developing arterial AID models using loop detector data, with some value obtained by augmenting this data with probe vehicle data (see Table 4). 5.3. Comparison of detector configurations for simulation model variables The performances of the best performing networks for each detector configuration, as a function of selected simulation parameters, are presented in Table. These results clearly show that the performance for detector configuration 2 was far better than for detector configuration 1, with substantially better detection rates
achieved for all simulation model variables. This is a particularly welcome finding as it implies that AID models can be developed without the need to modify existing traffic detection infrastructure available on arterial roads. 5.4. Inclusion of speed data In an effort to further improve incident detection performance, the inclusion of speed data in the detector configuration 2 data set, for a probe vehicle penetration rate of 20%, was investigated and was found to increase DR to 90% for a FAR of 0.5% (Fig. 5). With the inclusion of speed data, the detection rate on arterials (90%) becomes comparable to the widely accepted detection rates for freeway AID models and better than those reported for arterials in Hawas [8] and Zhang and Taylor [26]. This, however, poses one difficulty from a practical
Performance Envelope Curve Configuration 2 - Speed Data included 100% 90%
Detection Rate
80% 70% 60% 50% 40% 30% 20% 10% 0% 0.0% 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 1.0%
False Alarm Rate Fig. 5. Performance envelope curve for neural network with speed data.
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perspective because existing loop detection infrastructure on arterial roads is based on single loop detectors which are not capable of measuring speed. Therefore, methodologies and algorithms to deduce speed information from single loop detectors would have to be improved if the replacement of single loops with dual loop detectors becomes prohibitive from a financial point of view.
5.5. Conclusions and directions for future research The work reported in this paper has demonstrated the feasibility of developing a neural network model for detection of incidents on arterials using a fusion of loop and probe vehicle data. Simulated data was used to train and test a series of neural network architectures. The performance of each architecture was compared for two detector configurations and other simulation model variables to determine which features were associated with better performance. Probe vehicle penetration rates were also varied to determine the effects of increasing penetration rates. In keeping with much prior research (e.g. [9], it was found that an MLF neural network structure, the Jordan–Elman network, performed best. This network allowed inclusion of data from previous time steps, which was found to greatly enhance model performance. Of the two modelled detector configurations, neural network architectures trained using data from detector configuration 2 produced consistently better results. This was a welcome outcome as most arterial roads are already capable of providing data from detectors set up in this configuration. Overall, the best performance was obtained for the standard detector configuration when occupancy, flow, flow/occupancy and vehicle probe data for a penetration rate of 20% were used as inputs to the neural networks. The inclusion of speed data further improved the neural network’s performance (DR 90% for a FAR <0.5%). Although it is unlikely that speed data will be made available on all arterial roads in the short term due to the prohibitive cost of replacing loops, it is possible to develop advanced algorithms for estimating the speed from existing single loops. Finally, very few studies reported in the literature have attempted to test the performance of arterial AID models on a statistically reliable number of field incidents. Unfortunately, it was not possible to collect field incident data in this study either and this remains a high priority for future research in this field.
Acknowledgements The work reported in this paper is based on the research work of the second author while she was completing her Masters of Philosophy under the supervision of Dr. Dia. Kim has since graduated and is now working as Systems and Communications Manager for Parsons Brinckerhoff in Brisbane. Dr. Dia was Director of the ITS Research Laboratory at the University of Queensland at the time this work was carried out. He is now Principal Transport Consultant and Group ITS Leader of Aurecon Australia Pty. Ltd.
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Contents lists available at ScienceDirect
Information Fusion journal homepage: www.elsevier.com/locate/inffus
Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data Hussein Dia a,*, Kim Thomas b,1 a b
School of Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia Parsons Brinckerhoff, 69 Ann Street, Brisbane, Queensland 4001, Australia
a r t i c l e
i n f o
Article history: Received 10 August 2007 Received in revised form 24 June 2009 Accepted 7 January 2010 Available online 18 January 2010 Keywords: Automatic incident detection Neural networks Data fusion Microscopic traffic simulation
a b s t r a c t This paper describes the development of neural network models for automatic incident detection on arterial roads, using simulated data derived from inductive loop detectors and probe vehicles. The work reported in this paper extends previous research by comparing the performance of various data fusion neural network architectures and assessing model performance for various probe vehicle penetration rates and loop detector configurations. Data from 108 incidents was collected from loop detectors and probe vehicles using a calibrated and validated traffic simulation model. The best performance was obtained for detector configurations found on most existing road networks, with a detection rate of 86%, false alarm rate of 0.36% and probe vehicle penetration rate of 20%. Fusion of speed data further improved performance, resulting in an incident detection rate of 90% and a false alarm rate of 0.5%. The results reported in this paper demonstrate the feasibility of developing advanced data fusion neural network architectures for detection of incidents on urban arterials using data from existing loop detector configurations and probe vehicles. Ó 2010 Elsevier B.V. All rights reserved.
1. Introduction The high cost of congestion caused by incidents, mainly in terms of traffic delays, air pollution and deteriorated safety conditions, has prompted a growing worldwide interest in developing efficient and effective automated incident detection methods. Incidents are defined as non-recurring events such as accidents, disabled vehicles, spilled loads, maintenance work and other events that disrupt normal traffic flow and result in a capacity reduction of a facility. Such incidents account for a large percentage of the total delays on major road facilities around the world. For example, it has been reported that for every minute an incident remains un-cleared after its occurrence, it takes around four minutes for traffic to recover [17]. The benefits to be derived from early incident detection and quick response can drastically reduce traffic delays and improve road safety and real-time traffic control. Motorists can be informed by providing real time traveler information to allow for alternate routing of traffic and timely dispatch of emergency services. Intelligent transportation systems (ITS) technologies are structured to
* Corresponding author. Present address: Aurecon Australia Pty. Ltd., 32 Turbot Street, Brisbane Queensland 4001, Australia. Tel.: +61 7 3173 8670; fax: +61 7 3173 8001. E-mail addresses: [email protected] (H. Dia), [email protected] (K. Thomas). 1 Tel.: +61 7 3854 6200; fax: +61 7 3854 6500. 1566-2535/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.inffus.2010.01.001
address these needs through advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). For these systems to be effective, it is necessary to develop procedures for detecting incidents which are both reliable and quick to respond. Automated incident detection (AID) is essentially a pattern classification problem, where traffic data is analyzed and classified into one of two categories (an incident or non-incident pattern). A number of automatic incident detection algorithms have been developed or proposed over the last two decades for both the freeway and urban arterial road environments. The structure of these algorithms varies in the degree of sophistication, complexity and data requirements. Inductive loop detectors embedded in the road pavement are typically used to obtain the traffic data needed for these techniques. The loops provide volume (vehicles per time interval); occupancy (percent of time the loop is occupied by a vehicle) and in cases where dual loops are installed (such as on freeways but not necessarily arterials), speeds (kilometer per hour) are also obtained. Incident detection models based on time series analysis [2]; pattern comparison [14,13]; catastrophe theory [15]; probabilistic networks [10]; flow modelling of state variables [4]; wavelet techniques [20]; fractal dimension theory [23]; support vector machines [25]; artificial neural networks [16,3,9, 6,1,10]; Bayesian models [26] and fuzzy logic [8] were attempted for both the freeway and arterial environments and have been shown to exhibit varying levels of detection performance. Several
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other approaches, including video image processing, have also been developed and demonstrated. Each of these systems has its advantages and limitations with respect to cost, operational performance and area coverage. For the detection of lane-blocking incidents, the literature suggests that traffic data from inductive loop detectors feeding into an artificial neural network offers the highest detection rate and lowest false alarm rate [25,6]. The same literature, however, indicates that none of the techniques developed so far meets the operational needs of traffic management and control center operators, who demand a much lower false alarm rate to minimize operator overload. To overcome this problem, a neural network data fusion technique is proposed in this study to integrate traffic data from loop detectors with travel time data collected from probe vehicles. Probe vehicles can be used to collect time stamped locations and speeds. Buses, taxis and other commercial vehicles are commonly used for this purpose. Vehicles equipped with tags for electronic toll collection can also be used, provided the necessary infrastructure is available. The proportion of probe vehicles on the road network (referred to as penetration rate in this paper) plays an important role in the reliability of the probe vehicle data and its use for incident detection. The focus of this paper will be on incident detection on urban arterial roads, which are a more challenging environment than freeways due to the following factors:
j Freeways have limited access points whereas arterials have multiple access points. j Left and right turn movements at intersections make traffic movements more unpredictable. j Intersection control results in variable and periodic interruptions of flow, queuing of vehicles at intersections, and dispersion of vehicle platoons downstream. j Arterials usually operate at lower speeds which make lane changes easier, hiding the effects of an incident. j Arterials typically have more limited surveillance infrastructure than freeways. A number of studies have attempted the arterial incident detection problem (e.g. [7,21,19,18,9,11,12,25]). This study extends previous research by developing and testing various neural network data fusion architectures based on simulated loop detector and probe vehicle data for varying penetration rates [22]. Due to the difficulty of collecting field incident data, this study will rely on data generated from a microscopic traffic simulator capable of re-producing the impacts of lane-blocking incidents. This study also considers the impacts of different detector configurations. Two neural network architectures will be used: multi-layer feedforward (MLF) networks and modular networks. MLF networks consist of an input layer, linked by weighted connections to one or more hidden layers, which are in turn linked by weighted connections to the output layer. The hidden layers allow the network to learn the nonlinear relationships between the inputs and outputs. Modular networks consist of two or more modules or experts (Fig. 1), typically MLF networks, which operate independently but receive the same input data. Depending on the architecture used, an additional network called the gating network may control the degree to which each module’s output affects the final network output, giving greater priority to the best performing module. Despite their apparent complexity, modular networks can learn faster, by breaking a task down into smaller sub-tasks. Also, the
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performance of a single module does not affect the performance of the others. This is a particular advantage in real-world situations, where data may sometimes be unavailable. 2. Automatic incident detection systems Automatic incident detection (AID) is an integral part of incident management systems. AID systems involve two main components: a traffic detection system and an incident detection algorithm. The traffic detection system provides the traffic information necessary for detecting an incident while the incident detection algorithm interprets that information and ascertains the presence or absence of incidents. As was reported before, inductive loop detectors embedded in the pavement are typically used to obtain traffic data. The data comprises speed, flow and occupancy and is typically provided in 20-s cycles. Data of this type would form the input to an incident detection algorithm which would raise an alarm to indicate the presence of an incident on the facility. Part of the challenge in developing automated incident detection algorithms is to deal with the inherent ‘noisy’ character of the raw input data and to be able to differentiate recurrent from non-recurrent congestion. 3. Performance evaluation criteria For this research, incidents are defined as lane-blocking events that cause a reduction in the capacity of the road. When evaluating the performance of an incident detection algorithm, the following measures or evaluation criteria would ideally be computed for an independent data set of incidents that were not used in the development or calibration of the algorithm. 3.1. Detection rate (DR) This is defined as the number of detected incidents divided by the total number of incidents that occurred during the recorded time. Detection rates greater than 90% are desirable. An additional requirement is that the incident must be detected within a short time (e.g. 5 min) of the start of the incident. If the incident is not detected within that timeframe, it will be deemed undetected.
DR ¼
No: of detected incidents 100% Total no: of recorded incidents
ð1Þ
3.2. False alarm rate (FAR) If the incident detection algorithm raises an incident alarm, while in fact no incident was present, then this constitutes a false alarm. The false alarm rate (FAR) can be defined in two different ways, depending on whether it is computed as an on-line or offline false alarm rate. When computing the false alarm rate during on-line testing, the on-line FAR is defined as:
FARon ¼
No: of detection intervals which gave false alarms Total no: of intervals over which the model was applied 100% ð2Þ
When testing the algorithm performance based on historical data (i.e. off-line testing), the onset and termination of incidents can usually be determined. This allows for the determination of the start and end times of incidents and consequently for the computation of the number of times the algorithm was applied during non-incident conditions. Therefore, the off-line false alarm rate is defined as:
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H. Dia, K. Thomas / Information Fusion 12 (2011) 20–27
Fig. 1. Representation of a typical modular neural network architecture.
FARoff ¼
No: of detection intervals which gave false alarms Total no: of non-incident intervals in the entire data set 100% ð3Þ
To illustrate the difference between the on-line and off-line FAR, consider an algorithm that was tested for 100 intervals during which 10 intervals experienced a real incident and 15 other intervals experienced false alarms. The FARon would be (15%), and FARoff would be (15/90 or 16.7%). 3.3. Mean time-to-detect (MTTD) The time-to-detect (TTD) an incident is defined as the difference between the time the incident actually occurred (tio) and the time it was detected by the algorithm (tia). These times are usually not known precisely but estimates can be deduced from loop detector data or records kept by traffic control centers’. When evaluating the performance of the algorithm on multiple incidents, it is customary to report the average or mean time-to-detect (MTTD) of a set of (n) incidents. Therefore, the mean time-to-detection is defined as:
MTTD ¼
n 1X ðt ia t io Þ n i¼1
ð4Þ
3.4. Performance envelope curve area (PECA) The performance of incident detection algorithms in terms of DR, FAR and MTTD can vary depending on the selection of appropriate decision thresholds (DT) associated with each algorithm. The values selected for the decision thresholds play an important role in the classification of the input data and consequently in determining the incident detection performance of the model. The plot of DR against FAR (shown in Fig. 2) is called the performance envelope curve (PEC) of the model. At ideal performance, the DR would be 100% and the FAR would be zero. The quality of performance of the algorithm is demonstrated by the degree to which the PEC pushes upward and to the left as the DT values associated with the algorithm are relaxed. This can be quantified by the area under the curve (PECA) and the slope of the curve (particularly for low values of FAR). The PECA for a perfect discriminator will be 10,000 (i.e. 100 100). This procedure is particularly useful since it helps with evaluating the model’s performance based on the total representation of DRs, FARs and MTTD by using a single index, i.e. the area under the PEC.
The above definitions clearly show that both the DR and FAR measure the effectiveness of the algorithm while the MTTD reflects its efficiency. It can also be deduced that DR and FAR are positively correlated. In order to detect more incidents, the algorithm thresholds are relaxed and that causes some incident-free intervals to be interpreted as alarms. Since many false alarms are caused by random fluctuations in traffic flow, a persistence test is usually applied by testing multiple incident warnings in a few consecutive intervals before declaring an alarm. This method, however, reduces the efficiency of the algorithm because it results in an increase in the MTTD. Clearly, all the measures are all inter-related. The relative importance of the measures, however, is typically DR, FAR and MTTD [16]. When evaluating the effectiveness of AID algorithms under different conditions, it is common to compare their FAR for a fixed value of DR. Similarly, the efficiency of AID algorithms under different conditions can be evaluated by comparing their MTTD for fixed values of DR and FAR. 4. Model development Due to the difficulty of collecting field incident data, a traffic simulation model was used to generate the required loop detector and probe vehicle data. The main advantage of this approach is that once a model has been developed and validated, it is possible to generate data for a wide range of situations. For development of models of sufficient generality, a variety of incident conditions were required, with varying probe vehicle penetration rates. Data was collected using the PARAMICS traffic simulation tool. A network including two major arterials in Brisbane, including Coronation Drive and Milton Road (Fig. 3), was developed and validated [5,24]. Two detector configurations were simulated in this research (Fig. 4). Configuration 1 attempts to treat arterial links like freeways, and makes use of detectors installed downstream of intersections. This type of configuration is not currently available on most arterial roads. Configuration 2, on the other hand, is the standard configuration on arterial roads where loop detectors are located both upstream and downstream of intersections. For incident detection purposes, the section of road under surveillance (section of interest in Fig. 4) is typically bounded by the upstream (US) and downstream (DS) locations of the loop detectors. Vehicle identification units (VIDs), used to detect the probe vehicles, were modelled in the same locations as the loop detectors. To develop a standard input vector for configuration 2, detector site measurements were apportioned to the left turn, through and right turn movements at the upstream intersection.
H. Dia, K. Thomas / Information Fusion 12 (2011) 20–27
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Fig. 2. A performance envelope curve.
Fig. 3. Schematic of the testbed network.
Data was generated for a wide range of situations. Simulation model variables included: link length; incident location on link (upstream, midstream or downstream); incident duration (15, 30 or 60 min); incident severity (number of blocked lanes); and flow (>600 vph per lane or <600 vph per lane). Varying these features resulted in a set of 108 incidents for each configuration. Data was collected for each time step, irrespective of the traffic signal phase. A sample of the data is provided in Table 1 below. The data in this table is recorded in 20-s intervals and shows the speed (kph), flow (number of vehicles) and occupancy (percent of time the loop is occupied) for both the upstream and downstream detector stations. The last column in the table provides the incident conditions that were prevailing during that 20-s interval
within the section. A value of zero indicates incident-free conditions and a value of unity indicates incident conditions. The data collected from the simulator included a large number of samples which took into consideration the following parameters to ensure statistical reliability of data and the associated results. 4.1. Incident location The incident location within the link is an important parameter. If an incident occurs close to the downstream detector, it will take a while before the traffic close to the upstream detector is affected, so the performance of a detection system varies with the location of the incident relative to the detectors. Incidents were
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H. Dia, K. Thomas / Information Fusion 12 (2011) 20–27
Fig. 4. Modeled detector configurations.
modelled at three points on the link in question: upstream, midstream and downstream, between the US and DS detectors. 4.2. Incident duration The longer an incident lasts, the greater its effect on the traffic during and after the incident. Longer incidents are expected to have greater false alarm rates because traffic will still be affected once the incident ends. Three incident durations were modelled: 15 min, 30 min and 60 min. Each incident was set to occur thirty minutes after the beginning of the simulation. During the first 15 min of the simulation, the traffic on the network to builds up to the required levels. 4.3. Incident severity It is expected that the more severe the incident, the greater effect on the traffic, and the easier it should be to detect. Incident severity was modelled by locating incidents on: Lane 1: Closest to the kerb for slow traffic – low severity. Lane 2: Closest to the median for faster traffic – medium severity. Both Lanes 1 and 2: Totally blocking the road – high severity.
4.4. Traffic flow The greater the traffic flow on a link, the more measurable the effect of an incident on the traffic. Two traffic flow regimes were modelled to simulate peak and off-peak traffic. In simulations, the traffic flow is determined by a combination of a configuration
variable, the demand weight and the origin/destination matrix. Low simulated flows were less than 600 vehicles per hour (vph), while high flows were greater than 600 vph. In total, 108 incidents were simulated for detector configurations 1 and 2: two link lengths were modelled under high and low flow conditions. Incidents were simulated at upstream, midstream and downstream positions on each link, occurring in the slow lane; the fast lane and both lanes, for durations of 15, 30 and 60 min. The output files from each simulation were then merged into a single file, containing loop measurements and probe vehicle travel times. Data was aggregated to 20 s intervals, in keeping with freeway incident detection algorithms [6]. While speed measurements were collected from the simulated loops, they were not used as input, as most arterial loop detectors would not have speed data available. Instead flow divided by occupancy was used as a proxy for speed. With the exception of calculation of the flow/occupancy ratios, no input data was pre-processed. The 108 data sets were then randomly allocated to the training, cross validation and validation data sets. The training set comprised 50% of the observations and was used to determine the parameters of the model. The Cross Validation data set comprised 10% of the observations and was used to prevent overtraining of the model. Finally, the validation data set comprised 40% of the observations and was used to evaluate the performance of the trained model. Therefore, the validation data set comprised observations which were not used in model training or calibration. Identical allocations were made for configurations 1 and 2, to facilitate comparison of results. The NeuroSolutions software package was then used to develop and train a series of MLF and modular networks. Two types of MLF architectures were trained: generalized feed-forward (GFF) networks which have connections that jump over layers for speedier
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H. Dia, K. Thomas / Information Fusion 12 (2011) 20–27 Table 1 Sample simulation data. Time stamp (20-s)
Speed upstream (kph)
Flow upstream (veh)
Occupancy upstream (%)
Speed downstream (kph)
Flow downstream (veh)
Occupancy downstream (%)
Incident conditions (binary)
00:00 00:20 00:40 01:00 01:20 01:40 02:00 02:20 02:40 03:00 03:20 03:40 04:00 04:20 04:40 05:00 05:20 05:40 06:00 06:20 06:40 07:00 07:20 07:40 08:00 08:20 08:40 09:00 09:20 09:40 10:00
26.6 43.5 43.3 42.7 45.5 23.3 57.2 58.9 23.7 0 43.6 40.0 39.3 16.2 41.1 57.1 34.9 46.9 44.4 41.5 61.2 14.4 39.7 15.6 42.0 38.6 38.6 0 19.0 60.4 22.9
4 8 2 6 6 13 15 10 12 0 6 8 9 2 10 7 4 21 3 8 11 6 9 7 3 10 2 0 1 1 2
7.2 7.5 3.5 5.1 9.1 40.5 14.2 6.0 16.0 66.6 3.5 5.5 8.5 2.3 6.7 5.2 4.2 20.5 2.8 6.3 9.3 23.6 8.7 20.2 3.6 7.0 2.4 66.6 1.2 2.1 0.5
54.1 61.2 54.1 61.5 45.6 32.1 45.8 31.3 26.7 0 58.7 64.0 57.7 41.9 45.7 0 55.8 0 49.5 46.3 29.2 27.7 0 65.4 45.5 0 51.9 0 0 32.3 51.8
9 4 2 5 9 5 3 4 5 0 9 5 5 6 6 0 5 0 1 5 10 7 0 3 8 0 6 0 0 6 4
5.6 5.3 3.2 7.8 11.2 16.2 6.7 6.0 29.1 0 8.1 4.9 5.4 8.3 13.1 0 4.9 0 4.5 8.1 30.0 10.6 0 6.2 13.2 0 8.0 0 0 6.6 4.4
0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0
learning; and Jordan/Elman (JE) networks which have additional processing elements (PEs), called context units, that remember past activity using a configurable exponentially decaying recency gradient. In addition, two types of modular networks architectures were also trained: MOD1 networks which contained two-expert modules; and MOD2 networks which comprised four-expert modules and an integrator axon to provide memory of inputs. A large number of neural networks were trained, for various numbers of PEs in the hidden layers. Once trained, the performance of each network on the validation data set was determined. It was found that the best performance was obtained using upstream and downstream occupancy, flow and flow/occupancy ratio, supplemented with travel times for varying probe vehicle penetration rates. In general, performance improved with increased probe vehicle penetration rate. 5. Results 5.1. Detector configuration 1 A series of MLF and modular neural networks were developed for various probe vehicle penetration rates and hidden layer structures. The best performances for each neural network architecture are provided in Table 2 below. All mean times to detect were well below 3 min.
Best results were obtained in all cases for a probe vehicle penetration rate of 20%. The best detection rate of 59% for a false alarm rate of 0.5% was obtained using a Jordan/Elman MLF network, which allowed input of data from previous time steps. The best performance for the modular neural networks was obtained with the more complex MOD2 network, with a detection rate of 49%. Overall, and as can be deduced from Table 2, the results for detector configuration 1 were not promising. 5.2. Detector configuration 2 A series of MLF and modular neural networks were also developed for various probe vehicle penetration rates and hidden layer sizes for detector configuration 2. The best performances for each neural network architecture are provided in Table 3 below. All mean times to detect were also well below 3 min. The best results were obtained using a Jordan/Elman MLF neural network (DR 86% and FAR 0.36%). These results were obtained with a probe vehicle penetration rate of 20%. The PECAs were again reasonably high. The best performance for the modular neural networks was obtained with a probe vehicle penetration rate of 5%, with a detection rate of 77%. This result was again achieved with the more complex MOD2 network. The model performed substantially better with the inclusion of probe vehicle data. For the best performing JE network, there was only a marginal
Table 2 Best results for detector configuration 1. Neural network architecture
Persistence test steps
Time constant
Best DR (%)
PECA
Penetration rate (%)
Generalized feed-forward (GFF) Jordan/Elman (JE) Modular (MOD1) Modular (MOD2)
0 0 0 0
– 0.5 – 0.5
40 59 49 49
9476 9667 9715 9508
20 20 20 20
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H. Dia, K. Thomas / Information Fusion 12 (2011) 20–27
Table 3 Best detector configuration 2 results. Neural network architecture
Persistence test steps
Time constant
Best DR (%)
PECA
Penetration rate (%)
Generalized feed-forward (GFF) Jordan/Elman (JE) Modular (MOD1) Modular (MOD2)
0 1 0 0
– 0.8 – 0.8
54 86 35 77
9298 9697 9041 9732
0 20 0 5
Table 4 Detector configurations 1 and 2 performances for simulation model variables. Variable
Subtype
Configuration 1
Configuration 2
DR (%)
PECA
DR (%)
PECA
Link length
Coronation drive (long) Milton Rd (short)
82.0 38.0
9961 9527
96 76
9969 9531
Incident location on link
Upstream Midstream Downstream
53.5 65.5 48.0
9522 9692 9810
87 86 78
9555 9849 9586
Incident duration
15 min 30 min 60 min
77.0 44.5 53.0
9905 9529 9592
83 88 85
9790 9704 9622
Incident severity (lanes blocked)
Slow lane Fast lane Both lanes
77.0 69.0 8.5
9745 9732 9632
100 72 52
9998 9192 9936
Traffic flow
High (>600 vph/lane) Low (<600 vph/lane)
55.0 44.0
9347 9920
77 95
9199 9963
59.0
9667
86
9697
Overall performance
improvement in performance with increased probe vehicle penetration rate, demonstrating that, while desirable, probe vehicle data was not essential for that particular model. The results for detector configuration 2 are promising, and demonstrate the feasibility of developing arterial AID models using loop detector data, with some value obtained by augmenting this data with probe vehicle data (see Table 4). 5.3. Comparison of detector configurations for simulation model variables The performances of the best performing networks for each detector configuration, as a function of selected simulation parameters, are presented in Table. These results clearly show that the performance for detector configuration 2 was far better than for detector configuration 1, with substantially better detection rates
achieved for all simulation model variables. This is a particularly welcome finding as it implies that AID models can be developed without the need to modify existing traffic detection infrastructure available on arterial roads. 5.4. Inclusion of speed data In an effort to further improve incident detection performance, the inclusion of speed data in the detector configuration 2 data set, for a probe vehicle penetration rate of 20%, was investigated and was found to increase DR to 90% for a FAR of 0.5% (Fig. 5). With the inclusion of speed data, the detection rate on arterials (90%) becomes comparable to the widely accepted detection rates for freeway AID models and better than those reported for arterials in Hawas [8] and Zhang and Taylor [26]. This, however, poses one difficulty from a practical
Performance Envelope Curve Configuration 2 - Speed Data included 100% 90%
Detection Rate
80% 70% 60% 50% 40% 30% 20% 10% 0% 0.0% 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 1.0%
False Alarm Rate Fig. 5. Performance envelope curve for neural network with speed data.
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perspective because existing loop detection infrastructure on arterial roads is based on single loop detectors which are not capable of measuring speed. Therefore, methodologies and algorithms to deduce speed information from single loop detectors would have to be improved if the replacement of single loops with dual loop detectors becomes prohibitive from a financial point of view.
5.5. Conclusions and directions for future research The work reported in this paper has demonstrated the feasibility of developing a neural network model for detection of incidents on arterials using a fusion of loop and probe vehicle data. Simulated data was used to train and test a series of neural network architectures. The performance of each architecture was compared for two detector configurations and other simulation model variables to determine which features were associated with better performance. Probe vehicle penetration rates were also varied to determine the effects of increasing penetration rates. In keeping with much prior research (e.g. [9], it was found that an MLF neural network structure, the Jordan–Elman network, performed best. This network allowed inclusion of data from previous time steps, which was found to greatly enhance model performance. Of the two modelled detector configurations, neural network architectures trained using data from detector configuration 2 produced consistently better results. This was a welcome outcome as most arterial roads are already capable of providing data from detectors set up in this configuration. Overall, the best performance was obtained for the standard detector configuration when occupancy, flow, flow/occupancy and vehicle probe data for a penetration rate of 20% were used as inputs to the neural networks. The inclusion of speed data further improved the neural network’s performance (DR 90% for a FAR <0.5%). Although it is unlikely that speed data will be made available on all arterial roads in the short term due to the prohibitive cost of replacing loops, it is possible to develop advanced algorithms for estimating the speed from existing single loops. Finally, very few studies reported in the literature have attempted to test the performance of arterial AID models on a statistically reliable number of field incidents. Unfortunately, it was not possible to collect field incident data in this study either and this remains a high priority for future research in this field.
Acknowledgements The work reported in this paper is based on the research work of the second author while she was completing her Masters of Philosophy under the supervision of Dr. Dia. Kim has since graduated and is now working as Systems and Communications Manager for Parsons Brinckerhoff in Brisbane. Dr. Dia was Director of the ITS Research Laboratory at the University of Queensland at the time this work was carried out. He is now Principal Transport Consultant and Group ITS Leader of Aurecon Australia Pty. Ltd.
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