A user equilibrium-based fast-charging location model considering heterogeneous vehicles in urban networks

Inappropriate deployment of charging stations not only hinders the mass adoption of Electric Vehicles (EVs) but also increases the total system costs. This paper attempts to address the problem of identifying the optimal locations of fast-charging stations in the urban network of mixed gasoline and electric vehicles with respect to the traffic equilibrium flows and the EVs' penetration. A bi-level optimization framework is proposed in which the upper level aims to locate charging stations by minimizing the total travel time and the installation costs for charging infrastructures. On the other hand, the lower-level captures re-routing behaviours of travellers with their driving ranges. A cross-entropy approach is developed to deliver the solutions with different levels of EVs' penetration. Finally, numerical studies are performed to demonstrate the fast convergence of the proposed framework and provide insights into the impact of EVs' proportion in the network and the optimal location solution on the global system cost

A Lane-based Predictive Model of Downstream Arrival Rates in a Queue Estimation Model Using a Long Short-Term Memory Network

In this study, we develop a mathematical framework to predict cycle-based queued vehicles at each individual lane using a deep learning method - the long short-term memory (LSTM) network. The key challenges are to decide the existence of residual queued vehicles at the end of each cycle, and to predict the lane-based downstream arrivals to calculate vertical queue lengths at individual lanes using an integrated deep learning method. The primary contribution of the proposed method is to enhance the predictive accuracy of lane-based queue lengths in the future cycles using the historical queuing patterns. A major advantage of implementing an integrated deep learning process compared to the previously Kalman-filter-based queue estimation approach (Lee et al., 2015) is that there is no need to calibrate the co-variance matrix and tune the gain values (parameters) of the estimator. In the simulation results, the proposed method perform better in only straight movements and a shared lane with left turning movements

Impacts of bus stop location and berth number on urban network traffic performance

The effects of operational characteristics of the public transport system on the performance of the urban network traffic flow and the public transport system have been widely investigated at the local level. However, to the best of authors’ knowledge there is no attempt to investigate these characteristics at the network level. This study bridges this gap through the notion of network macroscopic fundamental diagram (NMFD). In particular, the effects of the bus stop location (i.e. far-side and near-side) and berth number are discussed at the network level through simulating different scenarios in the central business district (CBD) of the city of Christchurch, New Zealand. In consistent with the local level studies, the outputs show that the far-side bus stops result in better network performance (i.e. larger capacity and critical density range) and a lower median for the network average delay of car traffic. The near-side bus stops instead lead to a lower median for the public transport system. The results reveal that increasing the berth number improves the network capacity and median of the network average delay for both modes. Finally, the impacts of the combination of the far-side and near-side bus stop on network performance have been discussed

Multi-criteria appraisal of multi-modal urban public transport systems

This study proposes a multi-criteria decision making (MCDM) modelling framework for the appraisal of multi-modal urban public transportation services. MCDM is commonly used to obtain choice alternatives that satisfy a range of performance indicators. The framework embraces both compensatory and non-compensatory approaches including lexicographic, Simple Additive Weighting (SAW), technique for order preference by similarity to the ideal solution (TOPSIS) and Concordance Analysis. These methods are applied on survey data collected through a questionnaire in Teheran, Iran. The survey encompassed passengers, operators and the wider community and inquired about the perceived attributes of three urban public transport modes: regular bus (RB), bus rapid transit (BRT) and rail rapid transit (RRT). The aforementioned MCDM techniques were applied to rank the performance of the three studied transit modes. The outputs of this study are instrumental in supporting planning decisions and prioritizing measures to improve public transport services.Transport and PlanningCivil Engineering and Geoscience

Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways

© 2019 The current paper proposes a novel stochastic procedure for modelling car-following behaviours on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following model where a deep learning architecture is used to estimate a probability of lane-changing (LC) manoeuvres. To the best of our knowledge, this work is among the very few papers which exploit deep learning to model driving behaviour on a multi-lane road. The objective of this study is to establish a coupled stochastic continuous multi-lane car-following model using Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore, imaged second-based trajectories of the lane-changer and surrounding vehicles are used to identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated using a real-world high-resolution vehicle trajectory dataset. The results indicate that the prediction of the integrated SOVM is almost identical to the observed trajectories of the lane-changers and the following vehicles in the initial and the target lane. It has been found that the proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone

Comparison of On-Line Time-Delayed and Non-Time-Delayed Urban Traffic Control via Remote Gating

Recent studies demonstrated the efficiency of feedback-based gating control in mitigating congestion in urban networks by exploiting the notion of network fundamental diagram (NFD). The employed feedback regulator of proportional-integral(PI)-type targets an operating NFD point of maximum throughput to enhance the mobility in the urban road network during the peak period, under saturated traffic conditions. In previous studies, gating was applied directly at the border of the protected network (PN), i.e. the network part to be protected from over-saturation. In this work, the recently developed feedback-based gating concept is applied at junctions located further upstream of the PN. This induces a time-delay, which corresponds to the travel time needed for gated vehicles to approach the PN. The resulting extended feedback control problem can be also tackled by use of a PI-type regulator, albeit with different gain values compared to the case without time-delay. Detailed procedures regarding the appropriate design of related feedback regulators are provided. In addition, the developed feedback concept is shown to work properly with very long time-steps as well. A large part of the Chania, Greece, urban network, modelled in a microscopic simulation environment under realistic traffic conditions, is used as test-bed in this study. The reported results demonstrate a stable and efficient behaviour and improved mobility of the overall network in terms of mean speed and travel time.Transport & PlanningCivil Engineering and Geoscience

Bi-level optimization for locating fast-charging stations in large-scale urban networks

Although the electrification of transportation can bring long-term sustainability, increasing penetration of Electric Vehicles (EVs) may cause more congestion. Inappropriate deployment of charging stations not only hinders the EVs adoption but also increases the total system costs. This paper attempts to identify the optimal locations for fast-charging stations in the urban network considering heterogeneous vehicles with respect to the traffic congestion at different levels of EVs’ penetration. A bi-level optimization framework is proposed to solve this problem in which the upper level aims to locate charging stations by minimizing the total travel time and the infrastructure costs. On the other hand, the lower level captures re-routing behaviours of travellers with their driving ranges. Finally, numerical study is performed to demonstrate the fast convergence of the proposed framework

Using taxi GPS data for macroscopic traffic monitoring in large scale urban networks: Calibration and MFD derivation

A two-Fluid Model (TFM) of urban traffic provides the macroscopic description of traffic state. The TFMs parameters are hard to calibrate, particularly for the dynamic traffic conditions. This leads to the TFM often being used to compare the quality of service through the plot of stopping time versus trip time of the vehicles in the network. Recently, the taxi GPS data has been applied to predict the traffic condition at the network level. Despite the network-wide coverage of the taxi GPS probe data, the penetration rate of taxis in the network traffic is still a vital and challenging issue for traffic estimation purpose. It is necessary to estimate penetration rate of taxis by combining with other data sources. Here, we propose a novel approach to fill two gaps: TFM parameter calibration and the taxis penetration rate. This method stretches the description of TFM to a zone size. The method is applied to real Changsha city GPS data, calibrating the parameters. The macroscopic fundamental diagram of the large-scale city is derived. For the Changsha case, running speed is the super-linear power law of the fraction of running cars; the fraction of stopping time is nearly linear power law of density, which can be an alternative of the density. The proposed method enables the calibration of TFM parameters and macroscopic traffic monitoring at urban scale using only GPS data.Transport and Plannin

An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction

© 2019 Elsevier Ltd In this study, we develop a real-time and novel estimation method of lane-based queue lengths using two deep learning processes, which include of a Convolutional Neural Network (CNN) into a Long Short-Term Memory (LSTM). This approach not only outperforms the recently developed real-time estimation of lane-based queue lengths but also captures the spatiotemporal attributes of traffic. There are three primary challenges to design a deep learning based queue estimation model. First, the CNN and the LSTM are integrated to estimate lane-based queue lengths minimizing accumulative counting errors. Furthermore, short-term arrival patterns and long-term traffic demand trends are captured by the LSTM to improve the accuracy of estimates of cycle-based proportional lane-uses. In addition, imaged second-based occupancy rates and impulse memories are used to identify whether vehicular queues are remained at the end of each cycle by using the CNN. In numerical examples and case study, the integrated CNN – LSTM method shows excellent performance to estimate queue lengths in individual lanes in seconds compared to the other approaches applied in this paper. This work paves the way for the applicability of the deep learning to estimate traffic quantities in real-time for lane-based adaptive traffic control systems (ATCS). Furthermore, we will introduce offset in a signal plan and lane-based turning proportion on the proposed framework to explain vehicular spillbacks in an individual lane and a grid lock for pursuing coordinated traffic movements along arterials and in signalized urban networks