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The frequency response of phase-sensitive optical time-domain reflectometry (Φ-OTDR) is strictly limited by the repetition rate of the injected sampling pulses in conventional sensing scheme. To fully reconstruct an arbitrary vibration signal, the pulse repetition rate is required to be lower bounded by twice the maximum frequency of the vibration signal according to the Nyquist sampling theorem and at the same time upper bounded by the sensing fiber length to avoid generations of mixed Rayleigh backscattered signals from more than one optical pulse within the sensing fiber, making high-frequency vibration sensing over long distance impossible. In this work, we propose a novel method based on the serial multi-coset sampling (MCS) strategy which can recover sparse broadband vibration signals with sub-Nyquist sampling rates, which is thus able to break through the tradeoff between the measurable vibration frequency bandwidth and the sensing range. Pulses with non-uniform intervals are adopted to realize MCS for the Rayleigh backscattered lights along the entire sensing fiber rather than the traditional uniform pulse train. A continuous to finite (CTF) block is used to determine the active spectrum ranges of the sparse signal. By using a serial MCS sequence with an average sampling rate of 4.8 kHz, both a 4 kHz single-frequency sinusoidal signal and a 0.2 kHz bandwidth chirped signal with a center frequency of 4.6 kHz were sampled and recovered successfully in the simulation. In experiments, a single frequency vibration signal of 15 kHz and a chirped vibration signal with frequency ranging from 15 to 16 kHz were successfully identified and reconstructed over a sensing distance of 4 km using an average sampling rate of 21.6kHz, respectively, which however is not possible in conventional Φ-OTDR system. Furthermore, accurate identification of a broadband vibration signal with a maximum frequency of 6 kHz over a 10km-long sensing fiber shows that this method is also practically valid in long-distance sensing.

This paper studies a road tolling problem in which a road user travels from A to B either via road 1 by paying or via road 2 without charge. The route choice also obeys the user-equilibrium principle. However, the travel time is formulated as a strictly increasing function of the density through the velocity–density relationship or fundamental diagram, which significantly improves the formulation in the classical traffic assignment problem. Accordingly, the influence of toll rate on the road users, the administration department and the road runner is analyzed in detail, by assuming that the user demand is rigid and maximizing the total traffic flow and the runner’s benefits from the collection of toll fees.

The traffic safety of mountain highway has always been one of the taking point. This study aims to collect road design data in large-scale research and analyzes the accident risk of highway geometric alignment. Accordingly, a method based on satellite maps and clustering algorithms is proposed to calculate the geometric alignment of the highway plane and its longitudinal section. The reliability of the method was verified on Nanfu highway in Chongqing, China. The planar and longitudinal sectional geometries of the four highways in Chongqing were obtained by the above method, and the corresponding 36,439 traffic accidents which occurred from 2010 to 2016 were used as the research objects. The accident risk of the highway geometry was analyzed based on the SHAP and MLP theories. The results show that the fitting and prediction abilities of the MLP model are better than those of the negative binomial model, and its correlation coefficient is improved by 33.2%. In addition, compared with the negative binomial model, the MLP model can estimate more accurately and flexibly the complex nonlinear relationship between the independent and the dependent variables. Published version This work was jointly funded by the science and technology innovation program of the department of transportation, Yunnan province, China (No. 2019303 and 2021-90-2), the general program of natural science foundation, Yunnan province, China (No 2019FB072), the general program of key science and technology in transportation, the ministry of transport, China (No. 2018-MS4-102), and the National Engineering Laboratory Open Research Fund Project for Land Traffic Meteorological Disaster Prevention and Control Technology of China (NEL-2020-01).

Measurement accuracy of the optical frequency domain reflectometry (OFDR) will be deteriorated severely when the spatial resolution is improved due to the influence of the random noise, especially in long range distributed fiber optical sensing system. In order to eliminate the random noise, we have experimentally compared and analyzed the performance of OFDR system with and without the image processing methods in term of spatial resolution. The data arrays of the cross-correlation results are obtained after processing the collected distributed Rayleigh scattering signals along the sensing fiber, which can be used to construct a two-dimension (2D) image with the spectrum shift information induced by the strain. Thus, the image denoising methods can be applied to remove the random noise and improve the measurement accuracy. Based on this principle, two image denoising methods including the total variation method and the 2D Gaussian filter arithmetic are introduced to realize distributed sensing with high spatial resolution in long distance OFDR system. Experimental results show that the random noise can be suppressed and the outliers can be removed by the proposed methods. The strain gradients with high spatial resolution are identified effectively when the image denoising methods are used. Furthermore, spatial resolution with 1.3mm is achieved by the proposed methods with a wavelength sweeping rate of 20nm/s in 52m sensing fiber. The proposed methods provide a potential solution for OFDR system in long distance sensing with high spatial resolution, which is a significant step toward a high-performance OFDR system for practical applications.

Hydroplaning risk evaluation plays a pivotal role in highway safety management. It is also an important component in the intelligent transportation system (ITS) ensuring human driving safety. Water-film is the widely accepted vital factor resulting in hydroplaning and thus continuously gained researchers’ attention in recent years. This paper provides a new framework to evaluate the hydroplaning potential based on emerging 3D laser scanning technology and water-film thickness estimation. The 3D information of the road surface was captured using a vehicle-mounted Light Detection and Ranging (LiDAR) system and then processed by a wavelet-based filter to remove the redundant information (surrounding environment: trees, buildings, and vehicles). Then, the water film thickness on the given road surface was estimated based on a proposed numerical algorithm developed by the two-dimensional depth-averaged Shallow Water Equations (2DDA-SWE). The effect of the road surface geometry was also investigated based on several field test data in Shanghai, China, in January 2021. The results indicated that the water-film is more likely to appear on the rutting tracks and the pavement with local unevenness. Based on the estimated water-film, the hydroplaning speeds were then estimated to represent the hydroplaning risk of asphalt pavement in rainy weather. The proposed method provides new insights into the water-film estimation, which can help drivers make effective decisions to maintain safe driving.

In this study, a long-distance phase-sensitive optical time domain reflectometry (Φ-OTDR) with a flexible frequency response based on time division multiplexing is proposed and experimentally demonstrated. Distributed flexible frequency vibration sensing over long distance can be realized by reconfiguring the system layout in a time-division-multiplexed manner by re-routing the Rayleigh backscattered signals for segmented processing with extra erbium-doped fiber amplifiers added only instead of any other complex signal amplification or pulse modulation mechanisms. Through time-division-multiplexed reconfiguration, the tradeoff between sensing distance and vibration frequency response in Φ-OTDR system is largely relieved. Compared with the traditional system layout, the proposed system allows a flexible frequency response in each sensing fiber segment without any crosstalk among them. In experiments, distributed vibration sensing with a frequency response up to 4.5 kHz is achieved over a sensing distance of 60km by the proposed system, which is not possible in a conventional Φ-OTDR system. Furthermore, the frequency response flexibility of the proposed system is further verified by successfully identifying a vibration event with a frequency of up to 20 kHz at the end of a 52-km-long fiber.

In order to accurately predict the short-term traffic flow, this paper presents a k-nearest neighbor (KNN) model. Short-term urban expressway flow prediction system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the predication plan. At first, preprocess the original data and then standardized the effective data in order to avoid the magnitude difference of the sample data and improve the prediction accuracy. At last, a short-term traffic prediction based on k-NN nonparametric regression model is developed in the Matlab platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the predicting result is analyzed. Results show that the accuracy of the proposed method is over 90 percent and it also rereads that the feasibility of the methods is used in short-term traffic flow prediction.

This paper presents an energy saving Topology Control Algorithm based Game Theory (TCAGT) utilized in Wireless Sensor Networks (WSNs). The TCAGT is designed to be efficient energy-aware in information transmission of nodes in a WSN. This TCAGT employs game theory to efficiently optimize the radiating radius and transmitting range of nodes in WSNs according to the real-time energy strategies and has the following two critical features: (1) Introducing three game strategies of energy level and modeling the payoff value of a node in a WSN by self-energy efficiency and feedback from its neighbors; (2) Developing a cognitive efficient-energy link optimization algorithm based game theory for WSNs. This algorithm first determines the energy strategy of each node based game theory and then adapts the radiating radius of each node dynamically. The performance of the proposed TCAGT is compared to those of critical transmitting range-based topology control for different WSNs, and extensive simulation experiments on random test WSNs have demonstrated the potential of the proposed TCAGT for energy-conserving topology control in WSNs.

This paper presents an Energy Saving-oriented Routing Algorithm Based on Dijkstra (ESRAD) in Wireless Sensor Networks (WSN). The ESRAD is designed to be economic to energy consumption in information transmission between any of two sensor nodes in a WSN. By modeling energy consumption of nodes in a WSN as evaluation index of hop count judgment in least-hop routing algorithm, this study first presents an improved Energy Saving-oriented Least-hop Routing Algorithm (ESLHA) to search the shortest path of energy consumption with least nodes between origin node and object node. Then, in the light that the ESLHA does not take the energy consumption in transmission process into account, the ESRAD models the evaluation index of energy consumption at each node, which is the weight of each edge in a WSN, by introducing energy consumption at both node and transmission process, and employs a Dijkstra-based routing algorithm to efficiently search the reliable shortest path with least energy consumption. The performance of the ESRAD is compared to those of the ESLHA and the Dijkstra for different WSNs. Extensive simulation tests on randomly simulated WSNs have demonstrated the potential of the proposed ESRAD for searching the shortest path with least energy consumption to transmit information between any of two nodes in WSNs.

The activity-based model system is being coined as the next-generation demand-forecasting model. The agent-based transport simulation toolkit MATSIM is a fully integrated system that models decisions from the long term to the short term, and these decisions in MATSIM are activity-based models. This paper describes the application of MATSIM in a large-scale multiagent-based transport simulation for Shanghai, China. First, algorithms for integrating new data in Shanghai with MATSIM inputs such as synthetic population, facilities, and network are separately designed according to data characteristics. Then activity-based modeling is introduced to generate population plans, and activity replanning is employed to learn the better travel plans; a utility-based approach is used to model scoring for a plan. Finally, a full MATSIM-based simulation platform for the Shanghai scenario is built in detail. The scenario contains 200,000 synthetic persons simulated on a network with 50,000 links. The relaxed state of the simulation system is reached after 100 iterations of replanning procedures, and the mode choice, route choice, and activity time allocation modules are used to optimize agents’ activity plans. The feasibility of transport simulation in Shanghai by MATSIM is validated against the mode split and the observed counts. Extensive simulation results for the designed Shanghai simulation scenarios indicate that most of the observed counts match quite well with the traffic simulation volumes and demonstrate the potential of MATSIM for large-scale dynamic transport simulation.

This paper has focused on the analysis of pedestrian flow characteristics for 3 different groups of walking facilities including two-way escalator, ascending escalator combined with descending stairway, and descending escalator combined with ascending stairway between platform and concourse layer in an L-shaped interchange metro station. Data were collected by field observations to develop flow-density-speed relationship for each group of walking facility and analysis has been done to compare pedestrian flow characteristics and walking behavior on various groups of facilities. It has shown that walking facilities in the station discussed are not properly used for the pedestrian flow in rush hours and not safe, and an optimized usage of those facilities has been proposed according to the station structure and the analysis above. The data collected and the analysis done in this paper can be used as a basis for changing the usage of existing facilities, and will become an important reference for future pedestrian flow theory research in the same kind of metro stations.

The urban rail transit system in Chinese metropolis is developing into the network operation and structure optimization stage. With the high frequency of Critical equipment failure, equipment intelligent maintenance management needs highlighted. The paper puts forward a method of the evaluation of critical equipment health status in urban rail transit signal system with the needs of overhaul line in Shanghai urban rail transit. The method introduces the equipment service safety concept and defines the health status. Then the paper builds the multi-layer hierarchical structure model of equipment health status combining with considering the multi-dimensional influence factors of signal system equipment such as physical characteristics, economic characteristics and technical characteristics. Finally, the technical process of the fuzzy comprehensive evaluation of equipment health status is put forward. While take the switch which is the critical equipment of urban rail traffic signal system as an example to validate the proposed approach. The results show that the assessment consequence of the proposed method is consistent with the realistic expectations. The method can provide the reference for the diagnosis of hazard factors in service safety while quantitatively analyzing the equipment serving safety level.

In order to improve the service quality and traffic efficiency of bus rapid transit (BRT), transit signal priority (TSP) used in the adaptive traffic signal control system has been viewed as important opportunities for reducing overall traffic congestions and improving bus transit services. The first BRT line in Taichung, Taiwan is operating in July 2014, while fixed-time signal timings are used at all signalized intersections. According to the detection technology presently used in Taichung, a cross-way considered TSP method (CCTSP) is designed. The CCTSP, including vehicle detection, time prediction and strategy decision, aims at reallocating green times among the phases with considering the real-time traffic flow condition and the bus priority request. VAP, vehicle actuated programming, is a sub-module of micro simulation platform Vissim. The VAP based simulation platform is developed to evaluate TSPs in details. Testbed of the BRT line Network in Taichung-Port Road is established in VISSIM according to the collected BRT planning data in Taichung. Extensive VISSIM simulation results have demonstrated the potential of the developed CCTSP in balancing the BRT requests and overall traffic efficiency, and indicated that the signal priorities derived from CCTSP has better performance.

In response to the increased traffic congestion and worsening air pollution, using the portable emission measurement system (PEMS), this paper analyzes the relationship between emissions and speed, acceleration (qualitatively and quantitatively), and then establishes emission models of vehicles. The data in this paper is according to on-board emission measurement data of Beijing Jiaotong University. Furthermore, comparing the classic emission model (CMEM) based on Vissim, the models of data fitting in this paper are proved to be effective and more convenient. The obtained results provide a new approach for improving the classic emission models, which is more suitable for China or any other country/district.

With the significant improvement of living standards and the great change of social concepts, the demand of an urban rail transit information service (URTIS) for travelers is experiencing connotative change, calling for a return to people-based transportation. For the URTIS, the developing connotation is shifting from extensive construction to quality management and service - especially the individuation service that helps socially vulnerable groups (such as the old or the young) to participate in social life equally - which is the most urgent problem. This paper introduces the concept of inclusive design into the field of urban rail transit, and the inclusive design in urban rail transit information service (IDURTIS) is presented. Following the basic principles and approaches of inclusive design and taking the new demand of the URTIS into account, the general framework of the IDURTIS is developed. The IDURTIS is designed from the following four aspects: (1) design principles; (2) design goals; (3) design methods; and, (4) evaluation methods. Taking typical application terminals of the URTIS as study cases - such as ticket machines, barrier-free elevator markings and guide systems for the blind - designs of these three terminals are optimized by the proposed IDURTIS. According to the field-visit questionnaires, travelers' preference for these optimized designs are compared with the original designs of the above-mentioned three terminals, while key environmental factors of the IDURTIS are analyzed by the Analytic Hierarchy Process (AHP). Analysis results show that system management, hardware-facility improvement and policy support are three main factors that influence the planning and construction of inclusive environments in urban rail transit.

In major domestic cities, the development of urban expressway is network oriented. The traffic flow forecasting system is the important prerequisite and foundation for realizing real-time traffic management and control. However, the traffic flow forecasting research is mainly based on highways. Research and application of short-term traffic forecasting for urban expressway is severely insufficient. Therefore, the study of urban expressway flow forecasting is discussed and a short-term traffic flow forecasting system for urban expressway based on k-NN nonparametric regression model is proposed in this study. First, the study analyzes the characteristics and needs of the urban expressway traffic flow, introduces the k-NN nonparametric regression model, and designs the short-term traffic flow forecasting system based on k-NN overall. Then, the short-term urban expressway flow forecasting system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the forecasting plan. Finally, a short-term traffic forecasting for urban expressway based on k-NN nonparametric regression model is developed in the VS2010 VC++ platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the forecasting result is analyzed. Results show that the accuracy of the proposed method, under the five-minute interval, is over 90%, which best proves the reasonableness of the proposed forecasting model based on the k-NN nonparametric model.

Aiming at the problems of the local optimum of the genetic algorithm in solving traffic signal timing models, this paper presents an optimized traffic signal controller with a golden ratio-based genetic algorithm (TSCGRGA) for urban signalized intersections. This controller analyzes the correlation between the traditional performance indexes of traffic signal benefit, select time delays, stop rate, and throughput to establish relative evaluation indices of traffic signals; and a multiobject timing model of traffic signals is developed by weighted coefficient method. Then, an adaptive golden ratio-based genetic algorithm is presented to solve the optimized models, which uses real number encoding and introduces a golden ratio calculator to enhance the local optimal capability of genetic algorithm. Experiments are conducted on a typical urban isolated intersection and the performance of the developed model and algorithm is validated by comparison to those of fixed-time, actuated, and the real-coded genetic algorithm-based controllers for different traffic conditions. Extensive numerical calculation results have demonstrated the potential of the developed algorithm in the quality of solutions and time efficiency and indicated that the signal strategies derived from TSCGRGA have better performance.

This paper proposes the design and Paramics-based evaluation of a two-stage fuzzy logic traffic signal controller (TSTFC) for an isolated intersection. The fuzzy controller employs traffic intensity-based two stage fuzzy logic procedure, and the design principles of two-stage fuzzy controller are first presented. Then, the COM-based hybrid programming is employed to make the micro traffic simulator Paramics interact with fuzzy controllers developed by Matlab, and a Paramics-based simulation platform of TSTFC is developed via Paramics API. Finally, experiments are conducted on a typical urban isolated intersection, and the performance of the developed two-stage fuzzy control simulation model is validated by comparison to those of fixed-time, actuated, and classic fuzzy controllers for different traffic conditions. Extensive simulation results have demonstrated the potential of developed hybrid programming in time efficiency and simulating real traffic conditions and indicated that the signal strategy derived from TSTFC is more effective when traffic status at intersections is above low saturation.

This paper presents two adaptive two-stage fuzzy controllers for traffic signals at isolated intersections. Firstly, a two-stage combination fuzzy controller is designed. For traffic status variables in two-stage controller leading to the inefficiency of traffic states weakening under low traffic flow, the controller introduces 0–1 combination and determines the combination of traffic status variables of fuzzy controller's inputs from the perspective of structural optimization. Secondly, aiming at the problems of fuzzy controller parameter empirical settings and functional disability of learning, a two-stage fuzzy logic traffic signal controller with online optimization is proposed; this controller introduces the rolling horizon framework and optimizes the parameters of membership functions and controller rules by using hybrid genetic algorithm. The performance of two proposed models is validated via online Paramics-based simulation platform, and extensive simulation tests have demonstrated the potential of developed controllers for adaptive traffic signal control.

This paper presents the design and evaluation of a two-stage fuzzy logic traffic signal controller with online optimization for an isolated intersection. The controller is designed to be adaptive to real-time traffic demands and has the following two critical features: (1) designing a two-stage fuzzy logic controller. The fuzzy controller uses strategic and tactical vehicle loop detectors, placed respectively upstream and at the stop-line of the intersection on each approach, to measure approach arrival flows and estimate queues. This data is used to decide whether to extend or terminate the current signal phase. These decisions are made using the proposed two-stage fuzzy logic procedure. (2) Developing an adaptive optimization framework of fuzzy controller to adjust fuzzy membership functions and controller rules with on-line learning. This study models the performance index of average delays based on traffic status identification, and at regular time intervals, employs a hybrid genetic algorithm to efficiently yield the reliable solution through reappearance of statistical traffic flow. The performance of this controller is compared to those of fixed-time, actuated, classic fuzzy and two-stage fuzzy controller for different traffic conditions.

Passenger average travel time is an important parameter which reflects the operation efficiency of metro lines, especially those regional ones. The on-building Shanghai Metro Line 16 is the first typical regional metro line with three kinds of trains including non-stop, part-stop and regular train running in it. As different mixed running ratio would cause varied passenger average travel time and different transport capacity, it is important to analyze passenger average travel time between different origin-destination (OD) stations so as to understand the capacity of each part-route and thus formulate a dynamic operation strategy for this metro line. In this paper, simulation has been done via software Train Plan Maker (TPM) and offline MATLAB analysis program to analyze passenger average travel time between different OD stations with those three mode trains set at different mixed running ratios, and future operation suggestion for both short term and long term has been made according to not only the simulation results but the geography features along this metro line as well.

In the light of more traffic status variables are considered under low traffic flow in traffic intensity-based two-stage fuzzy control for urban traffic signals, thus leading to the inefficiency of traffic states weakening at isolated intersections. This paper introduces a two-stage combination fuzzy optimal controller for traffic signals at urban isolated intersections: the proposed controller adopts 0–1 combination; depending on the traffic status at intersections, the combination of variables of traffic status of fuzzy controller's inputs will be determined; and the single-stage controller is applied under low traffic flow, while the traffic intensity-based two-stage fuzzy controller is used under medium or high traffic flow. Experiment is carried on a typical urban isolated intersection and performance of proposed model and algorithm is validated via Paramics. Furthermore, four kinds of control strategies are extensively simulated on different simulations scenes. Simulation results indicate that the proposed controller is able to choose status variables based on traffic status features at isolated intersections and the signal strategy derived from the proposed controller is more effective.

In switchback station of the on-building Shanghai Metro Line 16, different kinds of trains such as part-stop and regular ones are designed to finish their switchback work there. As the operation time cycles for each train mode are different, certain kind of train should obey last in first out (LIFO) order when it uses switchback lane, this makes the train dispatching in such station more complex than urban metro lines. As for the turnout points which are essential for train dispatching in switchback station, more rigorous software algorithm and more reliable fault tolerance hardware mechanism must be employed in its interlocking control system so as to ensure the reliability. In this paper, according to the optimal lane use graph acquired by Train Plan Maker (TPM) simulation and the actual needs of Shanghai Metro Line 16, a design of turnout points interlocking control system based on fail-safe principle with multiple software and hardware redundancy designs integrated has been proposed.