A Simulation Study on Automated Transport Mode Detection in Near-Real Time using a Neural Network Rahul Deb Das Nicole Ronald Stephan Winter rahuld@student.unimelb. nicole.ronald@unimelb. winter@unimelb.edu.au edu.au edu.au Department of Infrastructure Engineering The University of Melbourne, Australia Abstract Detecting transport modes in near-real time is important for various context-aware location based services and understanding urban dynamics. In this paper we present a simulated study on detecting transport modes in near-real time using a neural network. We have shown how detection accuracy will vary with different temporal window sizes and different combination of modes. Since in urban environment transport modes move slowly due to traffic, considering movement attributes or kinematics alone for mode detection is not sufficient. That is why we investigated how spatial information can improve mode detection accuracy. The model has achieved 82%-95% accuracy using different simulation designs and proves its efficacy over other detection models. 1 Introduction Transport mode detection from trajectories has seen growing interest in research over last few years for its importance in various domains such as context-aware computing, location based services, understanding urban dynamics, travel demand surveys, traffic monitoring, and travel behaviour analysis. Traditionally travel modes have been surveyed in questionnaires, enabling also to capture additional knowledge including purpose of trip. Travel surveys, however, are burdensome, erroneous if made from memory, of low spatial detail, and reach only small sampling rates. Automation should overcome all these issues. Since the late 1990's, due to advancements in positioning and navigation technology, GPS started being used as a mean to collect travel data and assess its reliability and future possibilities (Wolf, 2000). Eventually, the use of GPS has increased as it has become more precise, portable and ubiquitous. Nowadays people themselves can track their movement trajectories using GPS and potentially other sensors on-board their smart phones (Periera et al., 2013). Most of the research on travel mode detection is based on rigid velocity based model. However, a velocity based approach is not always sufficient. For example, low speed conditions, which are nowadays typical in urban traffic due to traffic at capacity, or bad weather, produce mode ambiguities. In low speed traffic conditions, the speed of a bus is similar to a car or bicycle. Therefore, there is a need to consider various non-kinematic attributes along with movement attributes (kinematics) in order to detect different transport modes. Copyright © by the paper’s authors. Copying permitted only for private and academic purposes. In: B. Veenendaal and A. Kealy (Eds.): Research@Locate'15, Brisbane, Australia, 10-12 March 2015, published at http://ceur-ws.org Research@Locate '15 46 Existing transport mode detection research is mostly offline. That means modes are detected once a trip is completed from historical trajectories. Existing methods use the entire trip record in the form of trajectory and then separates the trajectory based on walking based segments into number of meaningful parts that correspond to respective transport modes. The hypothesis behind this research is that a neural network based model can adjust well in real time with varying movement behaviour and overcomes the mode ambiguity under low speed conditions. In detecting transport modes, movement characteristics derived from trajectories of the users are the raw data source. In this paper we will concentrate on trajectories of a single sensor, as provided by GPS enabled smart phones. Such GPS trajectories are unlabelled and come in raw format. Other sensors in the phone are neglected for the time being, but can easily be included in the model. A classifier is required that can detect the various transport modes used along each trip in real to near-real time. In this paper, a neural network based classifier has been tested. Our contributions are as follows- 1) We developed a simulated near-real time transport mode detection model based on a multi-layer perceptron neural network. 2) Earlier approaches to neural network based transport mode detection are mostly offline and did not use any spatial information. In this research we show how spatial information can increase the detection accuracy. 3) Selecting a proper temporal window for detecting transport modes in near-real time is critical and context dependent. In this paper we investigate how detection accuracy varies with different temporal window sizes, which helps in selecting a proper window size based on accuracy requirement. In this paper we also evaluate the performance measure of a multi-layer perceptron neural network in order to detect transport modes in near-real time. A real time model can detect the transport mode epoch by epoch basis (such as second by second). In this research, we simulated queries within short temporal window to detect a given mode instead of second by second basis. Hence, we call this model a near-real time mode detection approach. Detecting transport mode in near-real time is comparatively an emerging research area. In this paper we have developed a basic but intuitive near-real time mode detection model using a supervised learning approach. Real time mode detection can be useful for a number of applications. Applications include various context-aware location based services where the context could be a given transport mode. A petrol pump can distribute an electronic discounted coupon within its neighbourhood to all the private cars only. Detecting transport modes in real time can also help developing various context-aware mobile applications that can sense the modality and act accordingly. One instant could be developing a mode-dependent auto-answering service on smart-phones. If the mobile senses the owner is in driving mode then the auto-answer can automatically be enabled and helps driver to concentrate on the road rather than receiving any incoming call. Thereby this can help in reducing distractions on the road in order to reduce road accidents. This approach can also be helpful for urban planners or emergency service providers who want to know people’s mode choice at a given route or in a given region at a given time window for modeling travel demand or various spatio-temporal events. The paper is organized as follows. Section 2 discusses related works in transport mode detection from various perspectives. Section 3 discusses some of the basic terminologies and methodology. Section 4 demonstrates data preparation and experimentation. Section 5 shows the experimental results. Section 6 presents the discussion of these results, and Section 7 concludes the paper. 2 Related work Nowadays, smart-phones come with GPS enabled facilities. Since smart-phones are carried by the users almost everywhere and all the time, hence, this positioning facility can be utilized in order to collect trajectories without any external intervention. Once a GPS trajectory has been collected or is in the process of being collected, those trajectories or part there-of can be used for transport mode detection in real time or post-processing mode. In this regard, existing work is mostly based on post-processing of the trajectories or detecting modes offline. Existing literature shows a wide variety of post-processing algorithms and classifiers. Some of the approaches used the classification technique directly without segmenting the GPS trajectories (Byon et al., 2007; Dodge et al., 2009; Reddy et al., 2010). At the same time there are approaches applying segmentation of the entire trajectory into meaningful parts, corresponding to different modes, before classification (Mountain and Raper, 2001; Tsui and Shalaby, 2006; Schussler and Axhaussen, 2009; Zheng et al., 2010; Biljecki et al., 2012; Hemminki et al., 2013). Segmentation is done based on those points that show high probability of mode change. Mountain and Raper used change in speed and direction for segmentation in their work (Mountain and Raper, 2001). However, this approach creates ambiguities in certain cases where the vehicles move slowly and constrained to specific roads or Research@Locate '15 47 the rail or tram networks. Liao et al. used proximity to potential change points, such as bus stops or train stops for offline mode detection (Liao et al., 2007). However, GPS accuracy greatly varies in urban environments, depending on the number of satellites in view, time of the day and season, atmospheric conditions and surrounding sources of multipath effects. Other research used change in peaks of acceleration curves in order to segment the trajectory (Hemminki et al., 2013). However this approach also suffers from low ambiguity resolution, typically in low speed condition such as during bad weather or traffic congestion. Another common and intuitive way for segmenting the trajectory is based on detecting walking segments. The rationale behind this approach is the observation that a person generally walks between using two modes of transport. This approach has achieved promising results for segmentation (Tsui and Shalaby, 2006; Zheng et al., 2010; Biljecki et al., 2012). However this approach also fails when there is a quick mode change or walking is negligible. There have been a number of different algorithms for mode classification used so far. Zheng and colleagues used a decision tree, Bayesian Net, Conditional Random Field (CRF) and Support Vector Machine (SVM) in their work with 75% reported accuracy (Zheng et al., 2010). Gonzalez et al. used neural networks with 91% accuracy (Gonzalez et al., 2010). Some works are solely based on statistical measures (Patterson et al., 2003). As far as the input parameters or indicators are concerned, prior work mostly concentrated on velocity attributes (Bohte et al., 2008; Schussler and Axhausen, 2009). But in low speed condition velocity and acceleration are not sufficient to resolve the ambiguities. So, more recently, research has incorporated additional movement attributes including heading rate change and stop rates (Zheng et al., 2010). Vibration data has also been tested as an additional attribute with promising results (Ohashi et al., 2013). However, in order to achieve better accuracy and account for GPS signal loss others have used inertial localization and navigation sensors such as accelerometers, along with GPS sensors (Reddy et al., 2010; Hemminki et al., 2013; Ohashi et al., 2013). Byon and colleagues used GPS trajectories collected by GPS loggers to study detection accuracy in real time. However there focus was mainly on how accuracy varies with different sampling frequencies (Byon et al., 2009). They achieved high detection accuracy at 20 min temporal window. However they observed mainly four modes auto, walk, car, bus. Although Byon and colleagues developed two neural network models, one route specific and another one a universal model, they did not explore how spatial knowledge can help in detecting different modes. Also their approach is limited by their use of GPS loggers: they used instantaneous speed, acceleration, number of satellites in view for a given transport mode to train their classifier. Number of satellites in view depends on particular transport mode. Such as GPS device inside a bus is obstructed by the metallic body and ceiling and vertical windows limiting the number of satellites in view. Whereas a car would have wider front windshield that would allow stronger and multiple GPS signals. However when using smart-phones for detecting modes, instantaneous acceleration, number of satellites in view or horizontal dilution of precision values may not be available. Gonzalez and colleagues developed a neural network based mode detection model with a core focus on how to reduce streaming of movement data. Earlier work used a static and fixed data transmission procedure but that suffered from high financial costs associated with data transmission as well as computational overhead and storage issues. Gonzalez and colleagues proposed a novel critical point (CP) algorithm to transmit only the relevant GPS points during the trip (Gonzalez et al., 2010). Since movement states are uncertain and imprecise there are a couple of mode detection appraoch using fuzzy logic (Tsui and Shalaby, 2006; Biljecki et al., 2010). A fuzzy approach with three criteria and five to ten modes has been tested with an accuracy of more than 90% (Schussler and Axhausen, 2009; Biljecki et al., 2010). However these approaches are rule-based and involve fuzzy antecedents and fuzzy consequents (Zadeh, 1965; Mamdani and Assilian, 1975). This approach cannot adapt with different movement behaviour in real time. Since fuzzy logic based models are developed based on expert knowledge with predefined premise and consequents hence they are not scalable with new parameters and thus pose scalability and flexibility issues. In this paper we present a neural network based model that can learn in real time. A neural network based model is flexible and scalable. 3 Theory In this section we presented some basic definitions, concepts and methodology used in this research. 3.1 Raw Trajectory A raw trajectory is a set of spatio-temporal points arranged in a chronological order. This can be mathematically expressed as Tr ={Pi }: Pi= (xi, yi, zi, ti) ; i є [0, N] ;∀ i : (ti