A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Effective bus travel time prediction is essential in transit operation system. An improved support vector machine (SVM) is applied in this paper to predict bus travel time and then the efficiency of the improved SVM is checked. The improved SVM is the combination of traditional SVM, Grubbs’ test method and an adaptive algorithm for bus travel-time prediction. Since error data exists in the collected data, Grubbs’ test method is used for removing outliers from input data before applying the traditional SVM model. Besides, to decrease the influence of the historical data in different stages on the forecast result of the traditional SVM, an adaptive algorithm is adopted to dynamically decrease the forecast error. Finally, the proposed approach is tested with the data of No. 232 bus route in Shenyang. The results show that the improved SVM has good prediction accuracy and practicality

A Framing Link Based Tabu Search Algorithm for Large-Scale Multidepot Vehicle Routing Problems

A framing link (FL) based tabu search algorithm is proposed in this paper for a large-scale multidepot vehicle routing problem (LSMDVRP). Framing links are generated during continuous great optimization of current solutions and then taken as skeletons so as to improve optimal seeking ability, speed up the process of optimization, and obtain better results. Based on the comparison between pre- and postmutation routes in the current solution, different parts are extracted. In the current optimization period, links involved in the optimal solution are regarded as candidates to the FL base. Multiple optimization periods exist in the whole algorithm, and there are several potential FLs in each period. If the update condition is satisfied, the FL base is updated, new FLs are added into the current route, and the next period starts. Through adjusting the borderline of multidepot sharing area with dynamic parameters, the authors define candidate selection principles for three kinds of customer connections, respectively. Link split and the roulette approach are employed to choose FLs. 18 LSMDVRP instances in three groups are studied and new optimal solution values for nine of them are obtained, with higher computation speed and reliability

Application of Background Information Database in Trend Change of Agricultural Land Area of Guangxi

Abstract. Guangxi Province is one of the regions more serious desertification. This paper using ENVI image processing system, according to remote sensing image interpretation target mark and image spectral characteristics, found remote sensing interpretation model of the background information of forest, shrub and grass, agricultural land, surface water, towns, roads from TM and ETM data from 1988 to 2008, using supervision, unsupervised, maximum classification of natural law to retrieve background information from simple to complex interpretation of each classification. Meanwhile ,using humancomputer interaction to refine the results. The output shp format data Vector file of disaggregated data edited in the GIS system, and get the background information on various types of remote sensing data each time. The result showed that agricultural land area showed a decreasing trend , but change is not very significant

Guidance Compliance Behavior on VMS Based on SOAR Cognitive Architecture

SOAR is a cognitive architecture named from state, operator and result, which is adopted to portray the drivers' guidance compliance behavior on variable message sign (VMS) in this paper. VMS represents traffic conditions to drivers by three colors: red, yellow, and green. Based on the multiagent platform, SOAR is introduced to design the agent with the detailed description of the working memory, long-term memory, decision cycle, and learning mechanism. With the fixed decision cycle, agent transforms state through four kinds of operators, including choosing route directly, changing the driving goal, changing the temper of driver, and changing the road condition of prediction. The agent learns from the process of state transformation by chunking and reinforcement learning. Finally, computerized simulation program is used to study the guidance compliance behavior. Experiments are simulated many times under given simulation network and conditions. The result, including the comparison between guidance and no guidance, the state transition times, and average chunking times are analyzed to further study the laws of guidance compliance and learning mechanism

Continuance intention of autonomous buses: An empirical analysis based on passenger experience

The aim of this study was to understand passengers’ continuance use intention with respect to autonomous buses (ABs) based on actual riding experience. Therefore, an extended technology acceptance model taking characteristics of both autonomous driving and buses into account was proposed, and 576 passengers with ABs riding experience in China responded to the survey. Several findings were revealed. First, characteristics of buses (including perceived in-vehicle safety, service quality and general attitudes toward buses) had positive effects on continuance use intention. Second, perceived road safety was not directly associated with continuance intention but had positive effects on perceived usefulness. Third, two significant moderating variables were revealed, namely, past bus riding habits and driver reliance (i.e., the degree to which passengers considered it important for ABs to have a driver). These observations suggest practical implications for policymakers and automakers

Study on the optimization of VMS location based on drivers’ guidance compliance behaviors

Traffic guidance compliance behavior is influenced by many factors. The present study investigates the effect of Variable Message Sign (VMS) location on guidance compliance behaviors of drivers. Based on the State, Operator, and Result (SOAR) cognitive architecture, a SOAR agent framework of drivers’ traffic guidance compliance behavior is developed. The formation mechanism and the changes in the law of traffic guidance compliance behaviors of drivers, as well as the key factors of VMS location that affect drivers’ compliance behaviors are studied. These factors include the visual perception of drivers, memory representation, decision cycle, and learning mechanisms. Finally, traffic guidance compliance behavior based on the SOAR cognitive architecture is simulated multiple times to verify the effectiveness of different VMS locations. The simulation results show that setting the VMS a bit further away from the downstream intersection achieves better guidance effect

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited

Tunable Tribovoltaic Effect via Metal–Insulator Transition

Tribovoltaic direct-current (DC) nanogenerator made of dynamic semiconductor heterojunction is emerging as a promising mechanical energy harvesting technology. However, fundamental understanding of the mechano-electronic carrier excitation and transport at dynamic semiconductor interfaces remains to be investigated. Here, we demonstrated for the first time, that tribovoltaic DC effect can be tuned with metal-insulator transition (MIT). In a representative MIT material (vanadium dioxide, VO2), we found that the short-circuit current (ISC) can be enhanced by >20 times when the material is transformed from insulating to metallic state upon static or dynamic heating, while the open-circuit voltage (VOC) turns out to be unaffected. Such phenomenon may be understood by the Hubbard model for Mott insulator: orders’ magnitude increase in conductivity is induced when the nearest hopping changes dramatically and overcomes the Coulomb repulsion, while the Coulomb repulsion giving rise to the quasi-particle excitation energy remains relatively stable