Survey of Machine Learning Methods Applied to Urban Mobility

To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger's trip. In this work, we present a survey on urban mobility based on passengers' data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger's trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design

Analysis and comparison of publicly available databases for urban mobility applications

The challenges of multimodal applications can be addressed with machine learning or artificial intelligence methods, for which a database with large amounts of good quality data and ground truth is crucial. Since generating and publishing such a database is a challenging endeavour, there are only a handful of them available for the community to be used. In this article, we want to analyze three of these databases and compare them. We assess these databases regarding the ground truth that they provide, e.g. labels of the means of transport, and assess how much unlabelled data they publish. We compare these databases regarding the number of hours of data, and how these hours are distributed among different means of transport and activities. Finally, we assess the data in each public database regarding crucial aspects such as the stability of the sampling frequency, the minimum sampling frequency required to observe certain means of transport or activities and, how much lost data these databases have. One of our main conclusions is that accurately labelling data and ensuring a stable sampling frequency are two of the biggest challenges to be addressed when generating a public database for urban mobility

Smartphone-Based Localization for Passengers Commuting in Traffic Hubs

Passengers commute between different modes of transportation in traffic hubs, and the passenger localization is a key component for the well-funtioning of these spaces. The smartphone-based localization system presented in this work is based on the 3D step&heading approach, which is adapted depending on the position of the smartphone, i.e. held in the hand or in the front pocket of the trousers. We use the accelerometer, gyroscope and barometer embedded in the smartphone to detect the steps and the direction of movement of the passenger. To correct the accumulated error, we detect landmarks, particularly staircases and elevators. To test our localization algorithm, we have recorded real-world mobility data in out test station in Munich city center where we have ground truth points. We achieve a 3D position accuracy of 12 meters for a smartphone held in the hand and 10 meters when the phone is placed in the front pocket of the trousers

Modelling the impact of weather and context data on transport mode choices: A case study of GPS trajectories from Beijing

Over the years, researchers have been studying the effect of weather and context data on the transport mode choice. The majority of these works are based on survey data, however the accuracy of their findings relies on how respondents give accurate and honest answers. In this paper, the potential of using GPS trajectories as an alternative to travel surveys in studying the impact of weather and context data on transport mode choices is investigated in Beijing city. In the analysis, we apply both descriptive and statistical models such as the MNL and MNP models. Our findings indicate that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 C, the walking share increases by 27% and the bike share reduces by 21%, which is line with the results from several survey studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous as people are encouraged to use environmentally friendly travel mode choices such as the bike instead of the bus and car, which are known CO2 emitters. Moreover, due to a series of traffic restrictions introduced by the Beijing government during the 2008 summer Olympics, a decrease of 17.5% in the car share and an increase of 13% and 10% in the walking and bus shares, respectively are observed. These findings provide a scientific basis for effective transport regulation and planning purpose

Towards Vision Zero - V2X Communication for Active Vulnerable Road User Protection

Almost half of the casualties on European roads can be accounted to the group of the so-called Vulnerable Road Users (VRUs). The introduction of V2X Communication makes it possible to extend the awareness horizon of automated and autonomous vehicles, in order to avoid accidents with VRUs. In this paper we learn how V2X communication will protect VRUs, what requirements have to be met, what the key performance metrics are and how different communication and localization technologies perform. The Vision Zero goal is to reduce the road traffic casualties including VRUs to zero

Feasibility study: Magnetic-based passenger localization in train stations

Train stations are a key element of any transport network because they concentrate a large amount of passenger traffic on a daily basis. Passenger localization in train stations is though limited nowadays by the lack of satellite reception indoors and underground. A possible solution could be to use magnetometers, since they are embedded in today’s smartphones and are available in all urban environments. One of the most extended algorithms to perform magnetic localization is magnetic fingerprinting, however magnetic fingerprinting has not yet been proved viable in train stations. The aim of this article is to present a feasibility study of the possibility to apply magnetic fingerprinting in train stations to locate passengers. We have measured and analyzed the magnetic maps of different train stations in Munich, Germany. Our results show that, the functioning of the trains and the electric topology of the stations hinder the passenger localization using magnetic fingerprinting

Inertial Pocket Navigation System: Unaided 3D Positioning

Inertial navigation systems use dead-reckoning to estimate the pedestrian's position. There are two types of pedestrian dead-reckoning, the strapdown algorithm and the step-and-heading approach. Unlike the strapdown algorithm, which consists of the double integration of the three orthogonal accelerometer readings, the step-and-heading approach lacks the vertical displacement estimation. We propose the first step-and-heading approach based on unaided inertial data solving 3D positioning. We present a step detector for steps up and down and a novel vertical displacement estimator. Our navigation system uses the sensor introduced in the front pocket of the trousers, a likely location of a smartphone. The proposed algorithms are based on the opening angle of the leg or pitch angle. We analyzed our step detector and compared it with the state-of-the-art, as well as our already proposed step length estimator. Lastly, we assessed our vertical displacement estimator in a real-world scenario. We found that our algorithms outperform the literature step and heading algorithms and solve 3D positioning using unaided inertial data. Additionally, we found that with the pitch angle, five activities are distinguishable: standing, sitting, walking, walking up stairs and walking down stairs. This information complements the pedestrian location and is of interest for applications, such as elderly care

Optimal Sampling Frequency and Bias Error Modelling for Foot-Mounted IMUs

The use of foot-mounted inertial measurement units (IMUs) has shown promising results in providing accurate human odometry as a component of accurate indoor pedestrian navigation. The specifications of these sensors, such as the sampling frequency have to meet requirements related to human motion. We investigate the lowest usable sampling frequency: To do so, we evaluate the frequency distribution of different human motion like crawling, jumping or walking in different scenarios such as escalators, lifts, on carpet or grass, and with different footwear. These measurements indicate that certain movement patterns, as for instance going downstairs, upstairs, running or jumping contain more high frequency components. When using only a low sampling rate this high frequency information is lost. Hence, it is important to identify the lowest usable sampling frequency and sample with it if possible. We have made a set of walks to illustrate the resulting odometries at different frequencies, after applying an Unscented Kalman Filter (UKF) using Zero Velocity Updates. The odometry error is highly dependent on the drift of the individual accelerometers and gyroscopes. In order to obtain better odometry it is necessary to perform a detailed analysis of the sensor noise processes. We resorted to computing the Allan variance for three different IMU chipsets of various quality specification. From this we have derived a bias model for the UKF and evaluated the benefit of applying this model to a set of real data from walk

Inertial Pocket Navigation System for Pedestrians

There is nowadays a high demand of pedestrian navigation systems, which are integrated in safety-of-life Services such as disaster management for rescue personnel or locationbased services such as guidance in hospitals, airports or shopping malls. In this thesis, indoor and urban environments constitute the targeted scenarios and the navigation is performed with inertial and magnetic sensors due to their wide availability, light-weight and infrastructure-less nature. The research of this thesis aims at improving or covering specific gaps of pedestrian navigation areas to offer versatile pedestrian navigation systems for a wide range of applications. First, the use of magnetic field measurements to compensate the error of the gyroscopes and their effect on the estimated orientation has been comprehensibly analyzed. Quasi-error-free measurements with known bias values have been used combined with different magnetic field distributions and the results have been endorsed with medium-cost sensors' measurements. It is concluded that the use of magnetic measurements is beneficial to estimate the bias of the gyroscopes, yielding to reduced orientation estimation errors. However, the targeted scenarios commonly present perturbed magnetic fields and the biases' estimation process becomes prohibitively slow. Second, several algorithms have been proposed in this thesis that outperform the accuracy of the horizontal displacement estimation with respect to the state of the art. Additionally, an innovative vertical displacement estimation algorithm has been proposed and tested in real-world scenarios. This algorithm makes it possible for the first time to solve unaided 3D inertial navigation for non-shoe-mounted sensors. Lastly, a novel drift estimation algorithm capable of preventing positioning errors caused by orientation errors is proposed. The computation of the drift error is based on landmarks seamlessly detected using solely inertial measurements. Landmarks defining the building or City layout have been chosen to be stairs and corners. By re visiting these landmarks it is possible to compute the accumulated drift error, which is used to reduce the orientation error. The proposed algorithm has been extensively tested with quasi-error-free and medium-cost sensors' measurements. Two types of corrections, online and post-processed, are presented to adapt the pedestrian navigation system to the particular application