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Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.

Taxi demand prediction is valuable for the decision-making of online taxi-hailing platforms. Data-driven deep learning approaches have been widely utilized in this area, and many complex spatiotemporal characteristics of taxi demand have been studied. However, the heterogeneity of demand patterns among different taxi zones has not been taken into account. To this end, this paper explores zone clustering and how to utilize the inter-zone heterogeneity to improve the prediction. First, based on the pairwise clustering theory, a taxi zone clustering algorithm is designed by considering the correlations among different taxi zones. Then, both the cluster-level and the global-level prediction modules are developed to extract intra- and inter-cluster characteristics, respectively. Finally, a Multi-Level Recurrent Neural Networks (MLRNN) model is proposed by combining the two modules. Experiments on two taxi trip records datasets from New York City demonstrate that our model improves the prediction accuracy compared with other state-of-the-art methods.

This paper proposes a parallel recommendation engine, PRECOM, for traffic control operations to mitigate congestion of road traffic in the metropolitan area. The recommendation engine can provide, in real-time, effective and optimal control plans to traffic engineers, who are responsible for manually calibrating traffic signal plans especially when a road network suffers from heavy congestion due to disruptive events. With the idea of incorporating expert knowledge in the operation loop, the PRECOM system is designed to include three conceptual components: an artificial system model, a computational experiment module, and a parallel execution module. Meanwhile, three essential algorithmic steps are implemented in the recommendation engine: a candidate generator based on a graph model, a spatiotemporal ranker, and a context-aware re-ranker. The PRECOM system has been deployed in the city of Hangzhou, China, through both offline and online evaluation. The experimental results are promising, and prove that the recommendation system can provide effective support to the current human-in-the-loop control scheme in the practice of traffic control, operations, and management.

It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers' knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers' control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.

Accurate and real-time taxi demand prediction can help managers pre-allocate taxi resources in cities, which assists drivers quickly finding passengers and reduce passengers' waiting time. Most of the existing studies focus on mining spatial-temporal characteristics of taxi demand distributions, while lacking in modeling the correlations between taxi pick-up demand and the drop-off demand from the perspective of multi-task learning. In this article, we propose a multi-task learning model containing three parallel LSTM layers to co-predict taxi pick-up and drop-off demands, and compare the performance of single demand prediction methodology and that of two demands' co-prediction methodology. Experimental results on real-world datasets demonstrate that the pick-up demand and the drop-off demand do depend on each other, and the effectiveness of the proposed co-prediction methods.

Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects. In this paper, we propose a novel anchor-free method, called FII-CenterNet, which introduces the foreground information to eliminate the interference of the complex background information in traffic scenes. The foreground region proposal network segments the foreground based on boxes-induced segmentation annotation, and midground is proposed to provide rich edge information of the objects. In addition to foreground location, scale information is also introduced to improve the regression performance. Extensive experimental results on two public datasets verify the benefits of the introduction of the foreground information, and demonstrate that our FII-CenterNet achieves the state-of-the-art performance in both accuracy and efficiency.

IoT-driven intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient, safe, smart, reliable, and sustainable. The IoT provides the access and driving forces of seamlessly integrating transportation systems from the physical world to the virtual counterparts in the cyber world. In this paper, we present visions and works on integrating the artificial intelligent transportation systems and the real intelligent transportation systems to create and enhance “intelligence” of IoT-enabled ITS. With the increasing ubiquitous and deep sensing capacity of IoT-enabled ITS, we can quickly create artificial transportation systems equivalent to physical transportation systems in computers, and thus have parallel intelligent transportation systems, i.e. the real intelligent transportation systems and artificial intelligent transportation systems. The evolution process of transportation system is studied in the view of the parallel world. We can use a large number of long-term iterative simulation to predict and analyze the expected results of operations. Thus, truly effective and smart ITS can be planned, designed, built, operated and used. The foundation of the parallel intelligent transportation systems is based on the ACP theory, which is composed of artificial societies, computational experiments, and parallel execution. We also present some case studies to demonstrate the effectiveness of parallel transportation systems.

This paper is to discuss the state, trend, and frontiers of development of cyber-physical-social systems (CPSSs) in China. The demand for developing CPSS is discussed in detail, followed by the Artificial societies, Computational experiments, Parallel execution (ACP) approach for CPSS and knowledge automation. The development of ACP based on CPSS in transportation, energy, information, Internet of Things, and Internet of Minds (IoM) is discussed to demonstrate the cutting-edge applications in CPSS. Finally, the blockchainized IoM technology and the concepts of parallel society are described. This paper will contribute to the transition from the current social construct to a futuristic intelligent society.

Bus rapid transit (BRT) has been proved to be an effective tool to improve mass transit services. However, BRT's adaptive operations like management and scheduling under different scenarios are too complicated to implement using traditional methods. The ACP approach, which is based on holism and complex system theory and consists of artificial systems (A), computational experiments (C) and parallel execution (P), offers an efficient new method to cope with these complex systems, including BRT. In this paper, the parallel transportation management and control system for BRT (PTMS-BRT) is presented, which is designed and implemented using the ACP approach. PTMS-BRT integrates such functions as BRT's monitoring, warning, forecasting, incident management, and real-time scheduling, to provide its operations smoother, safer, more efficient, and reliable. It has been piloted successfully in Guangzhou BRT to demonstrate it as another successful example of parallel transportation systems.

The subway’s daily management and control are too complicated to be handled by using traditional methods. Based on the artificial systems, computational experiments, and parallel execution (ACP) approach, the Parallel Transportation Management and Control System for Subways (PTMS -Subway) is proposed. First, the dynamic status perception and management platform for subways (SPMP-Subway) is constructed, and artificial subway systems (ASS) are designed and constructed, and then they are validated by the real-time data from SPMP-Subway. Then, the design content and construction process of computational experiments platform are performed. Finally, through the interactions of parallel execution system between actual subway and its ASS, a set of practical management and control algorithms can be validated and improved. PTMS-Subway can implement those advanced functions, such as real-time monitoring, warning, forecasting, scheduling optimization, incidence management, and so on, to improve its reliability, efficiency, safety, and service level. SPMP-Subway and PTMS-Subway have been piloted in Subway Lines 1 and 2 in Suzhou, China, and achieved the expected results and benefits successfully.

Advancements in complexity, complex systems, and the intelligence sciences, particularly smart city technologies, have shown great potential in aiding to ease traffic congestion. The overall approach and the main ideas in building smart transportation for smart cities, particularly ACP (artificial system, computational experiment, and parallel execution)-based parallel transportation management and control systems (PTMS), are presented. PTMS can be expanded to the new generation of intelligent transportation systems. The main components of the proposed architecture include social signal and social traffic, ITS clouds and services, agent-based traffic control, and transportation knowledge automation. Some technical details of these components are discussed. Finally, one case study is introduced, and the effectiveness is analyzed.

This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range (also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application.

Based on the big-data collected from Space-Air-Ground, i.e. Space means satellite, Air means helicopter, the key technologies of novel ITS (Intelligent Transportation System) are investigated, including data acquisition sensor, dynamic data transmission, massive data storage, multi-source data fusion, massive data mining and analysis, etc. On this basis, the cloud computing platform of novel ITS is designed, including Space-Air-Ground bigdata acquisition & transmission subsystem, cloud computing platform, intelligent transportation application & service subsystem. With the help of the data visualization, data prediction, and decision making, the complete traffic big-data set including people (passenger, driver), vehicle, and road traffic environment, can create their core addedvalues. The applications of novel ITS include: providing transportation data services for traffic enterprise and business users, such as customized mining, and specific industry analysis; providing accurate transportation information services for the citizen; providing business model for all levels of users, such as data visualization and customized services.

A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it’s a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail.

Social activity-travel has gained more and more attention as it is a growing percentage of the whole travel. To study its generation mechanism and behavioral characteristics, social network data are usually essential. However, due to individual privacy, it is rather difficult for traditional methods such as questionnaires to collect abundant reliable data. Therefore, we propose a novel method to grow realistic social networks based on artificial transportation systems (ATS). By incorporating the activity-travel simulation provided by ATS and a new agent-based model for social interaction, the method takes into account human mobility to generate spatially embedded social networks. Human mobility shapes and impacts social networks dynamically but is usually ignored by related studies. A case study based on computational experiments is carried out to verify the method. The results indicate that the method can generate social networks with similar topological and spatial characteristics to real social networks.

This paper proposes a method based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach to build parallel public transportation systems (PPTSs). The framework and components of a PPTS are analyzed, and some details for building the PPTS are discussed. One prototype based on intelligent traffic clouds is established. One specific PPTS is developed for the Guangzhou 2010 Asian Games in the case study, and its effectiveness is verified through the evaluation of two evacuation plans for the Asian Games.

Aiming to comply with the requirement of parallel-transportation management systems (PtMS), this paper presents a short-term traffic flow prediction method for signal-controlled urban traffic networks (UTNs) based on the macroscopic UTN model. In contrast with other time-series-based or spatio-temporal correlation methods, the proposed method focuses more on using the substantial mechanism of traffic transmission in road networks and the topology model of the entire UTN. Furthermore, this approach employs a speed-density model based on the fundamental diagram (FD) to obtain more accurate travel times in links. In the comparison experiment, the microscopic traffic simulation software CORSIM is adopted to simulate the real urban traffic. The experiment results fully verify the outstanding performances of the proposed prediction method.

A streamlined parallel traffic management system (PtMS) is outlined that works alongside a redesigned intelligent transportation system in Qingdao, China. The PtMS's structure provides enhanced control and management support, with increased versatility for use in real-world scenarios.

Field data are important for convenient daily travel of urban residents, reducing traffic congestion and accidents, pursuing a low-carbon environment-friendly sustainable development strategy, and meeting the extra peak traffic demand of large sporting events or large business activities, etc. To meet the field data demand during the 2010 Asian (Para) Games held in Guangzhou, China, based on the novel Artificial systems, Computational experiments, and Parallel execution (ACP) approach, the Parallel Traffic Management System (PtMS) was developed. It successfully helps to achieve smoothness, safety, efficiency, and reliability of public transport management during the two games, supports public traffic management and decision making, and helps enhance the public traffic management level from experience-based policy formulation and manual implementation to scientific computing-based policy formulation and implementation. The PtMS represents another new milestone in solving the management difficulty of real-world complex systems.

The Artificial societies, Computational experiments, and Parallel execution (ACP) approach provides us an opportunity to look into new methods that address transportation problems from new perspectives. In this paper, we present our work and results of applying the ACP approach on modeling and analyzing transportation systems, particularly carrying out computational experiments based on artificial transportation systems (ATSs). Two aspects in the modeling process are analyzed. The first is growing an ATS from the bottom up using agent-based technologies. The second is modeling environmental impacts under the principle of “simple is consistent.” Finally, three computational experiments are carried out on one specific ATS, i.e., Jinan-ATS, and numerical results are presented to illustrate the applications of our method.

To overcome public transportation problems during the 16th Asian Games held in Guanhzhou China, a PtMS (Parallel Transportation Management System), a novel application of Intelligent Transportation Systems, was introduced for effective and convenient traffic management. Results show that PtMS has successfully enhanced public traffic management, raising it from experience-based policy formulation plus manual implementation to scientific computing-based policy generation plus implementation with intelligent systems.

A new traffic signal control system (TSCS) evaluation method that uses computational experiments based on artificial transportation systems (ATSs) is proposed in this paper. Some basic ideas of the method are discussed, i.e., generating reasonable travel demand, modeling the influence of environment, and designing communication interface. Using a 30-day computational experiment on ATSs, a case study is carried out to evaluate three TSCSs, which are implemented using fixed-time (FT), queue-based responsive (QBR), and adaptive dynamic program (ADP) algorithms, respectively. Aside from normal weather, three types of adverse weather, i.e., rain, wind, and fog, are modeled in the computational experiment. After analyzing aggregate data and detailed operating record, reliable evaluation results are obtained from this case study. Furthermore, several interesting phenomena are observed in this case study, which have yet to be noticed by previous work.

A game-engine-based modeling and computing platform for artificial transportation systems (ATSs) is introduced. As an important feature, the artificial-population module (APM) is described in both its macroscopic and microcosmic aspects. In this module, each person is designed similarly to the actors in games. The traffic-simulation module (TSM) is another important module, which takes advantage of Delta3D to construct a 3-D simulation environment. All mobile actors are also managed by this module with the help of the dynamic-actor-layer (DAL) mechanism that is offered by Delta3D. The platform is designed as agent-oriented, modularized, and distributed. Both modules, together with components that are responsible for message processing, rules, network, and interactions, are organized by the game manager (GM) in a flexible architecture. With the help of the network component, the platform can be constructed to implement a distributed simulation. Finally, four experiments are introduced to show functions and features of the platform.

One of the major challenges facing the 2010 Asian Games is the transportation problem. Because the games will use 58 existing game facilities and 12 new sports stadiums, which are located across the Guangzhou metropolitan areas (see Figure 1), safe and effective traffic control and transportation management will be essential to their success (see http://www.gz2010.cn/en/). The city already has a big issue with road congestion, caused mainly by the inadequate supply of transportation infrastructures. For example, between 2001 and 2008, the number of private passenger vehicles in Guangzhou increased 529 percent to 783 thousand, an annual increase of 26.9 percent. However, only 193.1 km are scheduled to be added to the total length of metropolitan road networks between 2007 and 2010, which is an increase of 5,528 km, a mere 3.6 percent. By 2010, the number of private passenger vehicles will increase to an estimated 1.261 million, but the space for new roads and transportation infrastructures is limited.1 Under such time and infrastructure constraints, Guangzhou has chosen intelligent transportation systems (ITS) to enhance and improve its traffic safety and efficiency for a better 2010 Asian Games.