A survey of advances in vision-based vehicle re-identification

This paper presents a novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads. The loop sensor proposed in this paper detects large (e.g., bus) as well as small (e.g., bicycle) vehicles occupying any available space in the roadway, which is the main requirement for sensing heterogeneous and lane-less traffic. To accomplish the sensing of large as well as small vehicles, a multiple loop system with a new inductive loop sensor structure is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle, motor cycle, scooter, car, and bus but also enables accurate counting of the number of vehicles even in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus, the suitability of the proposed sensor system for any type of traffic has been established.

A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic

Advancements in sensing and the Internet of Things (IoT) technologies generate a huge amount of data. Mobility on demand (MoD) service benefits from the availability of big data in the intelligent transportation system. Given the future travel demand or origin–destination (OD) flows prediction, service providers can pre-allocate unoccupied vehicles to the customers’ origins of service to reduce waiting time. Traditional approaches on future travel demand and the OD flows predictions rely on statistical or machine learning methods. Inspired by deep learning techniques for image and video processing, through regarding localized travel demands as image pixels, a novel deep learning model called multi-scale convolutional long short-term memory network (MultiConvLSTM) is developed in this paper. Rather than using the traditional OD matrix which may lead to loss of geographical information, we propose a new data structure, called OD tensor to represent OD flows, and a manipulation method, called OD tensor permutation and matricization, is introduced to handle the high dimensionality features of OD tensor. MultiConvLSTM considers both temporal and spatial correlations to predict the future travel demand and OD flows. Experiments on real-world New York taxi data of around 400 million records are performed. Our results show that the MultiConvLSTM achieves the highest accuracy in both one-step and multiple-step predictions and it outperforms the existing methods for travel demand and OD flow predictions.

Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions

Travel surveys are increasingly taking advantage of GPS data, which offer precise route and time observations and a potentially reduced response burden. In these surveys, travel diaries are usually constructed automatically where research on the employed procedures has been focused on mode identification. The goal of the research reported here was to improve trip purpose identification. The analysis used random forests, a machine-learning approach that had been successfully applied to mode identification. The analysis was based on GPS tracks and accelerometer data collected by 156 participants who took part in a 1-week travel survey in Switzerland that was completed in 2012. The results show that random forests provide robust trip purpose classification. For ensemble runs, the share of correct predictions was between 80% and 85%. Different setups of the classifier were possible and sometimes required by the application context. The training set and its input variables (feature set) of the classifier were ...

Trip Purpose Identification from GPS Tracks

Traffic congestion is a critical concern in most cities. Inefficient traffic control wastes time and fuel, and causes harmful carbon emissions, road accidents, and many economic problems. This paper proposes a cooperative traffic control framework for optimizing the global throughput and travel time for multiple intersections. Adjacent intersections are considered in analyzing their joint passing rates and attempting to maximize the number of vehicles traveling through a road network. The proposed framework provides fairness for each road segment and realizes the green wave concept for arterial roads. This paper extends previous studies by considering the passing rates of continuous road segments and coordinating traffic signals of multiple intersections. The simulation results show that the approach outperforms existing schemes in that it achieves a high global throughput, reduces the average waiting time, lowers the total travel time, and decreases average CO2 emissions. To verify the feasibility of the proposed framework, a wireless access in vehicular environments/dedicated short-range communications-based prototype for lane-level dynamic traffic control is designed and implemented.

Cooperative Traffic Control With Green Wave Coordination for Multiple Intersections Based on the Internet of Vehicles

This paper presents an effective multiple-inductive-loop pattern suitable for heterogeneous and less lane-disciplined traffic and its performance evaluation. Vehicle detection system based on conventional inductive loops works well only for lane-based and homogeneous traffic. A multiple-loop system for sensing vehicles in a heterogeneous and less lane-disciplined condition has been reported recently. The scheme proposed in this paper employs a new configuration, where all the loops are connected in series, which considerably reduces the system complexity and improves reliability. Each loop has a unique resonance frequency and the excitation source given to the loops is programmed to have frequency components covering all the loop resonance frequencies. When a vehicle goes over a loop, the corresponding inductance and resonance frequency will change. The shift in frequency or its effect in any/every loop can be simultaneously monitored, and the vehicles can be detected and identified as a bicycle, a motorcycle, a car, a bus, etc., based on the signature. Another advantage of this scheme is that the loops are in parallel resonance; hence, the power drawn from the source will be minimal. A prototype multiple-loop system has been built and tested based on the proposed scheme. The developed system detected, classified, and counted vehicles accurately. Moreover, the system also computes and provides the speed of the vehicle detected using a single set of multiple loops. The accuracy of the speed measurement has been compared with actual values and found to be accurate and can be used for real-time intelligent transportation system (ITS) applications under heterogeneous and less lane-disciplined (e.g., Indian) conditions.

An Efficient Multiple-Loop Sensor Configuration Applicable for Undisciplined Traffic

This paper presents a novel inductive loop sensor which detects large (e.g., bus) as well as small (e.g., bicycle) vehicles and help a traffic control management system in optimizing the best use of existing roads. To accomplish the sensing of large as well as a small vehicle, a multiple loop inductive sensor system is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle or motor cycle or car or bus but also enables accurate counting of the number of vehicles that too in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed using a virtual instrumentation scheme and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus the suitability of the proposed multi loop sensor system for any type of traffic has been established.

A multiple loop vehicle detection system for heterogeneous and lane-less traffic

This paper presents an inductive loop vehicle detection system suitable for heterogeneous and less-lane disciplined traffic. Vehicle detection system based on conventional inductive loop principle has been in use but works well only for lane based and homogeneous traffic. A multiple loop system that is suitable for sensing vehicles in a heterogeneous and less-lane disciplined condition has been reported recently. This paper proposes a new measurement scheme for the multiple loop system. According to the new scheme, all the inductive loops are connected in series and only two cables are required, instead of two per each loop, between the measurement unit and multiple loop system, there by reduces the system complexity. Each loop has a unique resonance frequency and the excitation given to the loops connected in series is programmed to have frequency components covering all the resonance frequencies of the loops. When a vehicle goes over a loop the corresponding inductance and resonance frequency will change. The shift in frequency or its effect for individual loops can be monitored simultaneously and the vehicles can be sensed and identified as bicycle, motor-cycle, Car, Bus, etc. A prototype multiple loop system has been built and tested based on the proposed measurement scheme. The system developed sensed, classified and counted the vehicles accurately.

A simple multiple loop sensor configuration for vehicle detection in an undisciplined traffic

This paper presents a suitable algorithm to classify vehicles detected by a multiple inductive loop system, developed for measuring traffic parameters in a heterogeneous and no-lane disciplined traffic. The proposed classification scheme employs Random Forest (RF) algorithm. This scheme is suited not only for classifying the detected vehicles as bicycle, motorcycle, scooter, car and bus but also for counting them accurately under a mixed traffic condition. The algorithm has been implemented and tested. Its performance has also been compared with other algorithms based on threshold values and signature patterns. The threshold, signature and RF based algorithms use the number of loops a vehicle occupies as an important factor for classification. Results from a prototype system developed show that the RF based algorithm provides better accuracy compared to the threshold based and signature based methods.

S. Sheik Mohammed Ali

Papers

A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic

An Efficient Multiple-Loop Sensor Configuration Applicable for Undisciplined Traffic

A multiple loop vehicle detection system for heterogeneous and lane-less traffic

A simple multiple loop sensor configuration for vehicle detection in an undisciplined traffic

Application of random forest algorithm to classify vehicles detected by a multiple inductive loop system