A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions

Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.</p

Finding the Optimal Bus-Overtaking Rules for Bus Stops with Two Tandem Berths

Overtaking rule is a key factor for the estimation of bus discharge flow and bus delay at stops. In general, there are four kinds of overtaking rules, namely no-overtaking, enter-overtaking, exit-overtaking and free-overtaking. This paper studies a two-berth tandem bus stop in a saturated state and proposes calculation models for the maximum bus discharge flow and average berth blocking time under different overtaking rules. Cellular automata simulation is applied to verify the model&rsquo;s reliability. Then the influence of bus dwell time characteristics and overtaking rules are analyzed. Results show that overtaking has a positive impact on the maximum bus discharge flow and average berth blocking time to a certain extent. If only one overtaking behavior is allowed, the exit-overtaking rule is recommended. The study reveals that overtaking behavior plays an important role in bus service level and operational efficiency. Bus-overtaking rules are suggested to be changed with different bus flow states to obtain the optimal berth effectiveness

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures

Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework

Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient &lambda;, the shift factor b, and the rotation factor c. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten&ndash;Jagannathan&ndash;Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations

Characterizing Critical Transition State for Network Fundamental Diagram

Macroscopic Fundamental Diagram (MFD) reveals the relationship between network accumulation and flow at the macroscopic level. The network traffic flow state analysis is a fundamental problem for the MFD-based applications. Theoretical and experimental investigations have provided insights into the dynamics and characters of traffic flow states. Although many empirical studies had been conducted in the field of MFD, few studies were dedicated to investigate the network traffic flow states with field data. This study aims to develop a data-driven method based on time series analysis of MFD state points to characterize critical transition state (CTS) of network traffic flow using field data. The proposed method was tested in a real network of Kunshan City, China. The test results showed that the CTS points can be well captured by the proposed method. The identified CTS points distinguished the traffic states between free-flow state and optimal accumulation state, and the optimal accumulation state was characterized. The day-to-day pattern of CTS points was investigated by the Gaussian Mixture Model-based clustering model. An extended application of real-time identification of CTS points was also discussed. The proposed method is helpful to understand the temporal evolution process of network traffic flow and provides potentials for developing more reliable network traffic flow management strategies, such as optimizing traffic signal plans and developing strategies for congestion tooling

A Resource for Inactivation of MicroRNAs Using Short Tandem Target Mimic Technology in Model and Crop Plants

microRNAs (miRNAs) are endogenous small non-coding RNAs that bind to mRNAs and target them for cleavage and/or translational repression, leading to gene silencing. We previously developed short tandem target mimic (STTM) technology to deactivate endogenous miRNAs in Arabidopsis. Here, we created hundreds of STTMs that target both conserved and species-specific miRNAs in Arabidopsis, tomato, rice, and maize, providing a resource for the functional interrogation of miRNAs. We not only revealed the functions of several miRNAs in plant development, but also demonstrated that tissue-specific inactivation of a few miRNAs in rice leads to an increase in grain size without adversely affecting overall plant growth and development. RNA-seq and small RNA-seq analyses of STTM156/157 and STTM165/166 transgenic plants revealed the roles of these miRNAs in plant hormone biosynthesis and activation, secondary metabolism, and ion-channel activity-associated electrophysiology, demonstrating that STTM technology is an effective approach for studying miRNA functions. To facilitate the study and application of STTM transgenic plants and to provide a useful platform for storing and sharing of information about miRNA-regulated gene networks, we have established an online Genome Browser (https://blossom.ffr.mtu.edu/designindex2.php) to display the transcriptomic and miRNAomic changes in STTM-induced miRNA knockdown plants