Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030

As part of its Paris Agreement commitment, China pledged to peak carbon dioxide (CO2) emissions around 2030, striving to peak earlier, and to increase the non-fossil share of primary energy to 20% by 2030. Yet by the end of 2017, China emitted 28% of the world's energy-related CO2 emissions, 76% of which were from coal use. How China can reinvent its energy economy cost-effectively while still achieving its commitments was the focus of a three-year joint research project completed in September 2016. Overall, this analysis found that if China follows a pathway in which it aggressively adopts all cost-effective energy efficiency and CO2 emission reduction technologies while also aggressively moving away from fossil fuels to renewable and other non-fossil resources, it is possible to not only meet its Paris Agreement Nationally Determined Contribution (NDC) commitments, but also to reduce its 2050 CO2 emissions to a level that is 42% below the country's 2010 CO2 emissions. While numerous barriers exist that will need to be addressed through effective policies and programs in order to realize these potential energy use and emissions reductions, there are also significant local environmental (e.g., air quality), national and global environmental (e.g., mitigation of climate change), human health, and other unquantified benefits that will be realized if this pathway is pursued in China

Regional Expressway Freight Volume Prediction Algorithm Based on Meteorological Information

In the post-epidemic era, dynamic monitoring of expressway road freight volume is an important task. To accurately predict the daily freight volume of urban expressway, meteorological and other information are considered. Four commonly used algorithms, a random forest (RF), extreme gradient boosting (XGBoost), long short-term memory (LSTM) and K-nearest neighbour (KNN), are employed to predict freight volume based on expressway toll data sets, and a ridge regression method is used to fuse each algorithm. Nanjing and Suzhou in China are taken as a case study, using the meteorological data and freight volume data of the past week to predict the freight volume of the next day, next two days and three days. The performance of each algorithm is compared in terms of prediction accuracy and training time. The results show that in the forecast of freight volume in Nanjing, the overall prediction accuracies of the RF and XGBoost models are better; in the forecast of freight volume in Suzhou, the LSTM model has higher accuracy. The fusion forecasting method combines the advantages of each forecasting algorithm and presents the best results of forecasting the freight volumes in two cities

Consolidating Bus Charger Deployment and Fleet Management for Public Transit Electrification: A Life-Cycle Cost Analysis Framework

Despite rapid advances in urban transit electrification, the progress of systematic planning and management of the electric bus (EB) fleet is falling behind. In this research, the fundamental issues affecting the nascent EB system are first reviewed, including charging station deployment, battery sizing, bus scheduling, and life-cycle analysis. At present, EB systems are planned and operated in a sequential manner, with bus scheduling occurring after the bus fleet and infrastructure have been deployed, resulting in low resource utilization or waste. We propose a mixed-integer programming model to consolidate charging station deployment and bus fleet management with the lowest possible life-cycle costs (LCCs), consisting of ownership, operation, maintenance, and emissions expenses, thereby narrowing the gap between optimal planning and operations. A tailored branch-and-price approach is further introduced to reduce the computational effort required for finding optimal solutions. Analytical results of a real-world case show that, compared with the current bus operational strategies and charging station layout, the LCC of one bus line can be decreased significantly by 30.4%. The proposed research not only performs life-cycle analysis but also provides transport authorities and operators with reliable charger deployment and bus schedules for single- and multi-line services, both of which are critical requirements for decision support in future transit systems with high electrification penetration, helping to accelerate the transition to sustainable mobility

Optimizing Sustainable Transit Bus Networks in Smart Cities

Urban mobility has been facing several challenges in the recent years due to the increasing populations and private vehicles ownership, which led to several negative environmental and social impacts in big cities. In this dissertation, we investigate how decision support systems can enhance the role of transit bus networks in the transition to more sustainable and convenient urban mobility

Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated

Traffic modeling, forecasting and assignment in large-scale networks

Today, the development and evaluation of traffic management strategies heavily relies on microscopic traffic simulation models. In case detailed input (i.e. od matrix, signal timings, etc.) is extracted and incorporated in these simulators, they can provide valuable traffic state predictions. However, as this type of information is almost never available at the large-scale and traffic represents chaotic behavior in saturated networks, microscopic simulation models remain intractable and unstable. An alternative is a recently discovered network traffic model; macroscopic fundamental diagram (MFD). Nevertheless, large-scale traffic management strategies remain a big challenge partly due to unpredictability of choices of travelers (e.g. route, departure time and mode choice). Part I of the thesis is an attempt to fill this gap. Chapter 2, 3 and 4 elaborate new aspects of large-scale traffic modeling, and integrate route choice behavior into the modeling. Chapter 2 proposes a dynamic traffic assignment (DTA) model to establish equilibrium conditions in multi-region urban networks where the modeling is done through MFD dynamics. The method handles the stochastic components of the aggregated model through a sampling approach. In addition, the assignment model enables us to consider the response of drivers to changing traffic conditions in an aggregated modeling framework. Chapter 3 extends the DTA model presented in Chapter 2 to a route guidance system, where drivers are given a sequence of subregions to follow. Two aggregated models, region- and subregion-based models, are introduced to develop the guidance scheme and to test its effect, respectively. Notably, the challenge here is to translate certain variables across the traffic models without a loss of significance and assure certain degree of consistency. Chapter 4 extracts and reconstructs aggregated route choice patterns through an extensive GPS data set from taxis in a mega city. Observed GPS trajectories are first grouped together to provide a physical evidence for consistent route patterns. Second, in order to investigate the consistency of equilibrium assumptions considered in Chapter 2, observed trajectories are replaced with shortest path trajectories, and aggregated route choice patterns are reconstructed. Part II introduces novel travel time prediction and variability models. Travel time is a crucial performance measure in assessing the efficiency of transportation systems, and it provides a common index for both practitioners and travelers. Chapter 5 develops a travel time prediction model that jointly exploits traffic flow fundamentals and advanced data mining techniques. The prediction method detects the congestion patterns through the identification of active bottlenecks, and clusters the days with similar traffic patterns. This approach basically allows the model to train its predictions with relevant historical data sets. The method is applicable in oversaturated conditions and consistent with physics of traffic flow. Nevertheless, travelers not only consider travel time on average, but also value its variation. Day-to-day travel time variability, addressing the travel time variations of vehicles crossing the same route at the same period of time on different days, reveals interesting patterns. Departure time periods with similar mean travel times in the onset and offset of congestion exhibit quite different variance values. This phenomenon causes counter-clockwise hysteresis loops on the mean-variance curves. Chapter 6 investigates the empirical implications of hysteresis shape within the context of day-to-day travel time variability