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Purpose: To investigate the effect of curcumin on spinal cord injury (SCI) in a rat model. Methods: SCI was induced in the rats using mid thoracic spinal cord compression, after which curcumin was injected intraperitoneally. Western blotting was used for assay of expressions of apoptotic proteins, viz, IL-1β, NF-κB p65, TLR4, TNF-α, LC3, Bax and Bcl-2. Malondialdehyde (MDA) and myeloperoxidase were measured using standard methods. Neuronal loss in spinal cord tissues was determined with TUNEL staining and NeuN labelling. Results: Curcumin treatment significantly (p < 0.05) suppressed SCI-mediated upregulation of myeloperoxidase activity and increase in MDA level in rat spinal cord. The reduction of glutathione (GSH) and superoxide dismutase (SOD) activities in the spinal cord of SCI rats were suppressed by curcumin treatment. Curcumin treatment also led to a significant (p < 0.02) increase in the proportion of NeuN positive cells and marked reduction in TUNEL positive cells, but it decreased caspase-3 in the spinal cord tissues of SCI rats. Moreover, curcumin reversed the effect of SCI on protein expressions of Bax and Bcl 2 in a dose-based manner. There was marked curcumin-induced decline in CD11b and GFAP levels in the spinal cord tissues of the SCI rats. Conclusion: These results demonstrate that curcumin protects rats against SCI via inhibition of oxidative stress-mediated neuronal apoptosis. Therefore, curcumin may be useful for the development of an effective treatment for spinal cord injury.

Dockless bike-sharing (DBS) is a novel and prevalent bike-sharing system without stations or docks. DBS has the advantages of convenience and real-time positioning, whereas it brings about some problems such as bike over-supply, disordered parking, and inefficient rebalancing. Forecasting usage and bike distribution are critical in the rebalancing operation for maintaining DBS inventory. By dividing the virtual stations through K-means clustering and processing the four-week Mobike journey data of Nanjing, China, the data of usage and bike count in the 4000 virtual stations are identified. Random forest (RF) is developed to predict the real-time passenger departure, passenger arrival and bike count in the virtual stations. The operation analyses indicate that there is a positive correlation between bike count and usage. RF provides accurate predictions of usage and bike distribution, and almost outperforms five benchmark methods. Forecasting bike distribution is more challenging than forecasting usage because of the volatility of many factors. The results also suggest that bike distribution forecasting based on the usage gap prediction is better than that based on the departure and arrival prediction. This study can help DBS companies in dynamically rebalancing bikes from over-supply regions to over-demand regions in a better way.

China has been witnessing prominent demographic ageing because of its sustained low fertility (one-child policy) and mortality rates. In 2017, nearly one in four elderly adults in the world live in China. The rapid increase of the elderly population is supposed to dramatically influence the urban and transportation system. Active travel plays an important role for the ageing Chinese population to sustain their mobility and wellbeing. To provide suitable policy implications for age-friendly travel environments in China, this study investigates how the built environment affects active travel behavior. Particularly, we explore the influences of built environment on daily active travel frequency and time expenditure while taking into account travel attitudes. A zero-inflated ordered probit model and a Cox proportional hazards model are respectively estimated based on the Nanjing Travel Survey data. Results show that the social and cultural contexts exert pronounced impacts on the travel pattern of Chinese older people. Specifically, it is found that the living pattern of co-residence, and the proximity to market, park/square, and chess/card room are influential in shaping active travel patterns. In addition, the built environment shows larger effects on the active travel behavior of older adults than on that of young people. Attitudes towards active travel are not significant in explaining the senior's travel behavior, indicating limited self-selection effects. The findings will offer insights to establish effective and appropriate land use strategies and public facility distribution for the elderly during the Chinese urban renewal process.

Health care accessibility is a vital indicator for evaluating areas where there are medical shortages. However, due to the lack of population data with a satisfactory spatial resolution, efforts to accurately measure health care accessibility among older individuals have been hampered to some extent. To address this issue, we attempt to measure accessibility to health care services for older bus passengers in Nanjing, China, using a finer spatial resolution. More specifically, based on one month's worth of bus smart card data, a framework for identifying the home stations (i.e., a passenger's preferred station near their residence) of older passengers is developed to measure the aggregate demand at the bus stop scale. On this basis, a measurement that integrates the Gaussian two-step floating catchment area (2SFCA) and the adjusted 2SFCA methods (referred to as the adjusted Gaussian 2SFCA method) is proposed to measure accessibility to health care services for older people. The results show that: (1) almost all home stations experience inflated demand, especially those located in the suburbs; (2) despite abundant health care resources, home stations in urban districts are rarely identified as high accessibility stations, due to high demand densities among the older population; and (3) more attention should be paid to two types of home stations – those with a medical institution and those with bed shortages, respectively. The first type is predominantly distributed in the periphery of the city, in the suburbs where the travel time required to access the nearest health care service by bus is longer. The second type is mostly located in the outskirts of urban districts and in the central area of one suburb. These findings could help policy makers to implement more appropriate measures to design and reallocate health care resources.

Bus stops provide accessibility to public transit service, whereas they also influence the efficiency of mobility due to the extra dwell time. The decision of bus stop locations is a tradeoff between access coverage and mobility. This paper formulates a biobjective optimization model of bus stop placement problem at the network level. Two objectives are pertinent to the improvement of mobility: minimization of total dwell time at stops and minimization of total number of bus stops. Access coverage is constrained to ensure a certain level of accessibility. The issue of stop congestion and its effect on road traffic flow is also considered in the model. The model is applied to a case study of the bus network in urban areas of Yancheng, China. The results show that the proposed model can be efficiently solved by CPLEX to obtain Pareto optimal solutions for real-case problems.

Smart card (SC) data has become one of the major data sources for transit passengers’ behavior analysis, network modeling, and control optimization. Origin–destination (O–D) estimation has been recognized as a requisite step before utilizing the smart card data to investigate transit passengers’ spatiotemporal dynamics or conduct other SC data-based transit modeling. In the recent decade, the extant literature has proposed various trip-chain-based methods for transit O-D estimation using SC data. However, one problem of the conventional trip-chaining estimation approach has been noticed but not paid enough attention to: O-D estimation of single transactions cannot be conducted since the trip-chain method generally requires at least two trip records per day to proceed with. Such a flaw in the classic trip-chain approach might lead to a considerable amount of data loss and inaccurate O-D estimation. This paper improved the existing trip-chain O-D estimation method by introducing a new framework based on the Minimum Entropy Rate (MER) criterion. The proposed MER-based method adopts a similar mechanism of noise reduction in information theory. The basic idea of our approach is to infer the alighting location of single trips using alternative stops that preserve passengers’ travel regularity exhibiting in their mobility sequences. Our enhanced approach can estimate alighting stops for single trips with decent accuracy, thus preventing a potential massive data loss. Moreover, the study also provides an in-depth insight into the relationship between entropy rates estimated using trip sequences and passengers’ travel regularity. The estimation results can further benefit future transit studies with reliable data sources.

In China, low-income commuters are usually concentrated in peripheral settlements outside downtown areas, where travel services are inadequately provided. These commuters are dependent on fewer travel options, considering their affordability. Based on the recognition that public transit is an important mode to enhance low-income commuters’ travel mobility, a comprehensive attitude-based market segmentation analysis was performed to identify distinct market segments to best serve the needs of each segment and to develop plans to increase transit usage. First, a detailed household survey was conducted in Fushun, China, to obtain commuters’ attitudes toward daily travel. Then, factor analysis was utilized to explore latent attitudinal factors. The structural equation modeling investigated the correlations between attitudes and public transit usage. The k-means clustering was then employed to partition the transit market into several subgroups. Finally, five segments of transit market with distinct attitudes were identified by three dividing variables, namely, the desire for comfort, the need for reliability, and environmental awareness. Low-income commuters in the same segment share homogeneous travel preferences while those in other segments possess different attitudes. The attitudinal characteristics, socioeconomic profile, and mode choice behavior in each segment were examined and discussed. Policies that best meet the needs of each submarket were proposed. These transit-related strategies included building a reliable operation environment, improving the level of service of existing facilities, implementing demand-response transit services, and providing public propaganda and education toward environmental protection.

Low-income residents can depend on fewer travel options and have restricted mobility. This paper analyzes low-income commuters’ mode choice behavior by using data from an activity-based travel survey in Fushun, China. An integrated choice and latent variable model is presented. The model uses the following latent attitudes: comfort, convenience, reliability, flexibility, safety, and environmental preferences. The inclusion of attitudes captures unobserved heterogeneity of the choice process with a better understanding of travel demands. Postestimation of the integrated model is applied to assess the responsiveness of preferences for various transportation modes to changes in policy-relevant variables. This assessment is done by calculating the elasticity and marginal effects of choice probabilities for the relevant attributes of travel preferences. The analysis indicates that individuals with high comfort preferences care more about walking environment, and they need solutions to enhance their walking experience. However, travelers preferring reliability are more likely to travel by public transit, and measures to inform commuters of real-time bus operation information were proposed. Commuters who emphasize environmental preference are more apt to cycle; therefore, probike strategies are recommended. Results of the analysis indicate that different actions should be taken to serve different preferences. The findings should be useful information for policy makers and transportation planners wanting to improve low-income commuters’ travel quality.

This paper reports that the sodium–potassium (Na–K) lidar was completed in November 2016 at Sao Jose Dos Compose, Brazil (23°S, 45°W), by the joint effort of the National Space Science Center, Chinese Academy of Sciences (NSSC) and Instituto Nacional de Pesquisas Espaciais (INPE). This system realized the Na and K metal layers simultaneously observe in Brazil, and this is the first instance of K layer detection in South America. Some of the key parameters and technologies have been optimized based on the Na and K layer dual-wave lidar in Beijing Yanqing station, such as improve technical parameters for receiving telescope, the narrow linewidth, efficient laser frequency doubling, the wavelength automatic locking techniques. By adopting these technologies, the output were 589 nm and 770 nm lasers, with high emission powers of 75 mJ and 83 mJ, respectively, and backscattered signals of Na and K layers with high signal quality were obtained. Observation data showed that the original echo photon count of the Na layer was approximately 42,486 (time resolution: 200 s, spatial resolution: 96 m) and the number of noise photons was 286 in a single data acquisition. The signal-to-noise ratio was up to 205:1. At the same spatiotemporal resolution, the original echo photon count of the K layer was approximately 1633, the noise photons were 38, and the signal-to-noise ratio was up to 40:1. The initial photocounts received has demonstrated that the Brazil K lidar has produced high quality signal with signal-to-noise level required by intended science studies. Moreover, the simultaneous phenomena of sporadic Na (Nas) and sporadic K (Ks), and the highly concentrated layers of atomic K have been observed, the K density in these narrow layers exceeds 1019 cm−3.

As the government pays more attention to energy conservation and emission reduction in the energy industry, the contradiction between economic cost and carbon emission reduction in the electric-coal supply chain becomes more and more serious, which needs to be solved urgently. Therefore, based on the theory of green supply chain, this research constructs a dynamic multi-objective model under the uncertain decision-making environment with the realization of the equilibrium of the total economic cost and carbon emission of electric-coal supply chain as the decision goal, and taking the coal supply capacity and and other practical requirements as the constraints. The following equilibrium model is applied to a electric-coal supply chain in the southwestern region of Y Province. With full consideration of different carbon emission reduction scenarios, the total economic cost and carbon emission balance plan of electric-coal supply chain under five different emission reduction scenarios were obtained, which verified the practicability and effectiveness of the equilibrium model. The results show that the equilibrium model can effectively alleviate the conflict between the economic cost and carbon emission reduction. Finally, based on data analysis, we put forward management suggestions from macro, meso and micro levels to better realize the greenization of electric-coal supply chain.

Low-income residents are dependent on fewer options for travel and have restricted mobility. This study examines the mode choice of low-income commuters based on an activity-based travel survey in Fushun, China. A model of integrated choice and latent variables was utilized to construct and test the significance of sociodemographics, activity attributes, and latent attitudinal variables. More specifically, a “multiple indicators, multiple causes” model was used to construct the latent variables postulated to be important for travel mode choice, and a multinomial logit model was employed to test the significance of explanatory variables. Results indicate that gender, age, and driving license possession play an important role in the formation of these underlying factors: comfort, convenience, reliability, flexibility, safety, and environmental preferences. On several accounts the latent-variable-enriched choice model outperforms the traditional choice model and provides insights into the importance of unobservable variables in the choice process. Preferences for the latent variables except flexibility are found to exert a significant influence on the mode choice of low-income commuters. Reliability is shown to be helpful in increasing the likelihood that low-income commuters will choose public transit. The sociodemographics and activity attributes also have important effects on the choice process. Findings should provide useful information to policy makers and transportation planners to improve the mobility of low-income commuters.

The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes.

In this paper, we proposed a new method to extract travel patterns for transit riders from different public transportation systems based on temporal motif, which is an emerging notion in network science literature. We then developed a scalable algorithm to recognize temporal motifs from daily trip sub-sequences extracted from two smart card datasets. Our method shows its benefits in uncovering the potential correlation between varying topologies of trip combinations and specific activity chains. Commuting, different types of transfer, and other travel behaviors have been identified. Besides, varying travel-activity chains like “Home → Work → Post-work activity (for dining or shopping) → Back home” and the corresponding travel motifs have been distinguished by incorporating the land use information in the GIS data. The analysis results contribute to our understanding of transit riders’ travel behavior. We also present application examples of the travel motif to demonstrate the practicality of the proposed approach. Our methodology can be adapted to travel pattern analysis using different data sources and lay the foundation for other travel-pattern related studies.

Tumor necrosis factor (TNF) -α plays a crucial role in rheumatoid arthritis (RA)-related bone loss disease. The main mechanism of action of RA induced bone loss is the significant inhibitory effect of TNF-α on osteoblast differentiation. TNF-α inhibits osteoblast differentiation mainly by activating nuclear factor (NF) -κB signaling pathway. Owing to the crucial role of TNF-α and NF-κB in the inhibition of osteoblast differentiation, they are considered as targets for the development of therapeutic drugs. In the present study, we evaluated the NF-κB inhibitor Boswellic acid (BA) and its derivatives in the regulation of osteoblast differentiation and the molecular mechanism. Based on the cell model of TNF-α induced inhibition of osteoblast differentiation of MC3T3-E1, the regulatory role of BAs was studied. The result of MTT assay indicated that bone morphogenetic protein (BMP) -2, TNF-α, or acetyl-11-keto-β-BA (AKBA) impact no significant effect for cell viability of MC3T3-E1. The results of alkaline phosphatase (ALP activity assay and real-time polymerase chain reaction indicated that AKBA blocked TNF-α-induced inhibition of the expression of osteoblast markers, suggesting that AKBA rescued osteoblast differentiation from TNF-α-induced inhibition. Additionally, AKBA stimulated the BMP-2-induced expression of osteoblast markers, suggesting that AKBA promotes osteoblast differentiation directly. The results of western blotting and luciferase assay indicated that N-κB signaling was activated by TNF-α. The overexpression of NF-κB component p65 in MC3T3-E1 was found to attenuate the positive effect of AKBA in osteoblast differentiation, suggesting that AKBA potentiates osteoblast differentiation by inhibiting NF-κB signaling. Collectively, AKBA promotes osteoblast differentiation by inhibiting TNF-α and NF-κB. Our study revealed a new discovery of AKBA in regulating osteoblast differentiation, and demonstrated that AKBA may be a potential anabolic agent in the treatment of RA-derived bone loss disease.

Regional bus scheduling is necessary to urban public transport that is complicated by the necessity of assigning trips that belong to several routes to buses located at different depots while reducing fleet size and operating costs. Considering the reality of emergencies that may interfere with the ability of vehicles to complete trips on time, it is reasonable to use fuzzy numbers to express uncertain delay times. Based on this idea, this paper proposes a chance-constrained programming model of regional bus scheduling that will reflect additional constraints such as the capacities of related depots and fueling needs. The objective of this paper is to maximize utilization of fleet vehicles. To overcome the defect of premature convergence in the particle swarm optimization algorithm (PSO), an improved PSO is proposed by using an organic fusion with group search optimization. Finally, an example demonstrates the correctness and effectiveness of the model and algorithm.

This research studies ways to coordinate timetables in a regional bus transit with multiple transport modes to satisfy numerous realistic constraints, such as maximum and minimum departure intervals, by considering transfer among buses, subways, and passenger special lines with intersecting routes. The primary objective is to minimize the total waiting time for nontransferring and transferring passengers in all stops, while the secondary objective is to maximize the number of berths for all vehicles arriving at stations during a certain period. The constraint method converts the problem into a single objective programming problem. Based on the characteristic of the model, this study proposes an improved bacterial foraging optimization (BFO) design, which defines solution coding, redesigns the heuristic procedure to initialize chromosomes randomly, and uses the “ladder” concept to enhance bacterial foraging operation to resolve the problem. Finally, a numerical example is provided to reveal the differences in schedule coordination between single and multiple transport modes as well as to analyze the influence of the best schemes of station capacity. Furthermore, the improved BFO is compared with other intelligent algorithms to verify the model as well as the accuracy and effectiveness of the algorithm.

Public bicycle acts as a seamless feeder mode in combination with the citywide public transit, as well as a competitor for the inner-city short trips. The primary objective of this study is to address the layout planning of public bicycle system within the attracted scope of a metro station. Based on the land use function, population, and bicycle mode share, bicycle rental stations are divided into three types, namely, the metro station, district station, and resident station, and later the quantity of bicycle facilities in each rental station is estimated. Then, the service stations are selected from these bicycle rental stations to provide the service of periodical bicycle redistribution. An improved immune algorithm is proposed to determine the number and locations of service stations and the optimal route options for the implement of redistributing strategy. Finally, a case study of Nanjing Tianyin Road metro station is conducted to illustrate the proposed model and clarify some of its implementation details.

Metro transit networks are constantly expanding to meet the growing travel demand that accompanies rapid urban growth. One main disadvantage of metro transport is its heavy reliance on access and egress transport and, hence, on the corresponding transfer facilities. Improvements to these transfer facilities therefore have the potential to increase use of the metro system while possibly alleviating the traffic load on the urban road network. Because bicycle transfer at metro stations—that is, using a bicycle as mode of access to or egress from the metro system—is underused, the determinants of the demand for bicycle transfer are investigated. Results and findings are valuable for designing policies aimed at improving metro ridership and for designing bicycle parking and transfer facilities at metro stations. To this end, several metro stations in Nanjing, China, were analyzed, and two stereotypical metro stations were selected for how well they represented the system. A large-scale survey was conducted on metro travelers' opinions on and use of bicycle transfer facilities, and data were collected on the current attributes of service groups, bicycle parking occupancy, and transfer mode alternatives. Furthermore, metro travelers' (latent) transfer preferences for bicycle rental facilities were investigated. Two transfer choice models were estimated to identify and quantify the determinants for bicycle transfer demand: one focuses on current walk–metro trips, and the other focuses on current bus–metro trips. The explanatory determinants are discussed, and relative weights are computed with multiple linear regression analysis.

Construction of the mass rapid transit system (MRT) is of great importance both to rationally utilizing the economical resource and optimizing the overall arrangement of urban land use. However, the decisionmaking process is always influenced by some subjective factors. In this paper, the data envelopment analysis (DEA) method is introduced to design an evaluation model for the decisionmaking process of 3 RMT systems. Due to excessive indices, the analytic hierarchy process (AHP) is used to deal with weighted processing in order to get synthetical indices to make evaluation simple. Relatively important coefficients are introduced to the target function of DEA to resolve the traditional DEA's dependence on the input-output data completely, which fails to reflect the actual important degree of the indices. It is testified that combined method of DEA/AHP is adaptive and maneuverable in the process of decisionmaking for MRT systems.

Understanding travel behavior of public transit users, especially special populations, is essential for the planning and management of urban public transit facilities and services. The wide use of automated fare collection systems makes it possible to gain large amounts of data on the usage of public transit. Based on smart card data for Suzhou, this paper analyzed travel behavior of special populations who are physiologically and psychologically different from other populations, such as the elderly, students and disabled. This paper starts with a detailed discussion of the structure of smart card data and presents a series of data pre-processing methods. After data cleaning, descriptive statistics and clustering were used for travel behavior analysis. The results show that behavioral characteristics not only differ between the special and general populations, but also vary among the elderly, disabled and students.

This paper proposes a new method for evaluating equity of bus spatial availability. The objective of using equity is to quantify and evaluate how bus service is distributed among population and employment locations, with the goal of significantly enhancing policy analysis. The essence of the measure is quantifying service supply satisfaction to traffic demand. In the demand portion, K-Means Clustering Analysis is conducted to determine the degree of traffic demand, while the level of bus spatial coverage is modified by street connectivity and practical bus-serviced area, in terms of supply. The method is then applied to the planned bus skeleton network of Changxing Center Area. The results of the case study suggest the demand supply inconformity (DSI) is effective and could be a useful reference to help decision-makers analyze and optimize the distribution of bus service in a more equitable manner.

A limited number of berths can result in a bus stopping upstream of a bus stop when the berths are fully occupied. When this traffic situation occurs, passengers waiting on the platform usually prefer to walk to the stopped bus, which leads to additional walking time before boarding the bus. To understand this situation, this paper divides the travel time at conventional bus stops into waiting time, additional walking time, and boarding time. The additional walking time was formulated based on the theory of queuing and probability. Field-measured and simulated data were designated to demonstrate the effectiveness of the proposed model. By analysing the results, it can be concluded that additional walking time will increase on a large scale when the number of berths at a bus stop exceeds the threshold of three. The theoretical analysis explains the inefficiency of bus stops with more than three berths from the perspective of passengers' additional walking time, which is significant in regards to the design of bus stops.

Transit passenger origin-destination (OD) data is usually obtained by an on-board survey or household survey. To avoid the disadvantages of a traditional survey method, this paper presents an OD matrix estimation method that estimates the OD matrix of transit passengers based on an OD matrix of bus stops, which was surveyed using the ticket survey method that can show the boarding and alighting stops of each passenger. According to land use and distance, the attaching relationship between zones and stops was established. The assignment weight (AW) was defined for the first time. The relationship between stop boarding-alighting and attaching zones population and land use was quantified. Finally, the AW model was calibrated, and the OD matrix of transit passengers was estimated, using the survey OD matrix of bus stops from the transit network optimization research of Changzhou as a given matrix. The model requires less data, calculates faster, and can avoid unnecessary iteration. The result was examined by means of questionnaire survey data, and it proved that the model is viable and reliable.

Bus Rapid Transit (BRT) has been increasingly regarded as a cost-effective mode of transportation. In order for BRT ridership to be sustainable, the planning of BRT stations must be coordinated with transit oriented development (TOD). Yet, no tool exists for planners to systematically model the BRT system with TOD. This paper proposes three stages of TOD (incubation, rapid growth, and maturity) that mirror the three BRT deployment phases (limited, moderate, and aggressive). The system dynamic (SD) approach has been suggested to model the progress of TOD and BRT deployment over time. The SD model consists of three modules: population, BRT, and TOD. The population and BRT modules have been developed and are described in this paper.

Using the travel-activity data collected from Shangyu, China, this paper develops multinomial Logit model with aggregate alternatives, including multiple size variables in representation for intermediate stop destination choices in the commute activity patterns. The authors use the amounts of facilities with different land use in the surveyed destinations as the size variables. Apart from the zonal attractions (size variables), locational factors and origin destination (OD) matching characteristics, Hanson accessibility are introduced into the models. The empirical results show that the commercial establishments are most likely to be chosen by intermediate stopping. And central business district (CBD) areas offer more opportunity to the intermediate stops. Moreover, the results show that the destination containing maintenance/leisure places compete with those nearby to win more intermediate stopping opportunity, which makes it more attractive in Shangyu, China. The findings of this paper provide some useful insights into the spatial activity-travel behaviors in the context of China.

In government policy, urban rail transit is regarded as the backbone of public transit of large cities in China. The stations with different patterns and attributes require diverse cohesion demands when integrating urban rail transit and surface transit. In this paper, the relationship between land use and a multimodal transport system within a station influence area is analyzed using the "surface-line-point" rule for station location. An influence index system with nine function-relative indexes is established based on land use and multimodal transit hierarchies. Principal Component Analysis (PCA) is used to extract the function feature for functional orientation of urban rail stations. Combined with investigation results, function and main index features, mode split for transferring to urban rail stations, and daily passenger flow distribution are analyzed for different station patterns. These characteristics provide necessary guidance for rail-surface transport systems.

Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide-ranging application, well beyond their design purpose. Relative to traditional manual survey, transportation planners only need to dig existed database to get both real-time and long term information using automated collection data. This paper studied the applications of data mining technique in the information processing of automated data collection systems in subway, and put forward a framework for data mining system. The author studied on the analysis method in subway passenger flow and travel information, based on current technology in the subway data collection. Using data mining techniques and statistical analysis, the subway OD matrix and capacity rate is derived from collection data, travel time distribution of passengers was carried out a detailed description, and it is of important significance in scheduling and management of metro systems.

The paper suggests a new mode of public transportation system, called "One Road One Line with Straight Driving", in order to change so many unreasonable aspects in traditional urban public transport system. Based on analyzing the implication and characteristics of "One Road One Line with Straight Driving" mode, this paper establishes the new mode's framework, including adaptability of the new mode, analysis of passenger flow, layout of network and stops, transit signal priority, intelligent public transportation dispatching.

Cost analysis models of public transport aimed at maximizing social benefits or minimizing total costs, considering both users and operators. To investigate optimal frequency of bus rapid transit (BRT), cost analysis models were established based on the demand data which is described in detail as a matrix of flows between every stops of BRT in a single line service. The optimal conditions for the frequency were established. Taking BRT lines 1 of Changzhou as an example, this paper examined the effect of this method on the optimal values of frequency and provided the suggested values of the frequency. Sensitivity analysis of users' cost and operators' cost were investigated respectively. The theoretical analysis and the example suggest that the cost analysis models in this paper can be used to obtain optimal frequency properly.

This paper aims to present a method to design the timetable for public transport (PT) vehicle along a corridor when travelers differ in the time at which they prefer to start trip and the schedule delay cost they incur from traveling earlier or later than desired. The problem of minimizing the total schedule costs is modeled with the assumption that distribution of Destination Target Time (DTT) is continuous and schedule delay cost parameters vary among different origins and destinations. The first-order optimality conditions are identified. An explicit solution is derived when DTT's are uniformly distributed in the population over part of the day.

Mass Rapid Transit (MRT) system's development has a far-reaching impact on configuring urban economic resources as well as optimizing land-use layout. Yet in traditional decision making process, mass impact factors might cause subjective-influence. In this paper, a Data Envelopment Analysis (DEA) method is proposed and three MRT decision modes [Subway, light rail transit (LRT), bus rapid transit (BRT))]are designed. Integrative index are weighed and obtained from Analytic Hierarchy Process (AHP) method to avoid too much index annoying decision making. Relatively important coefficients are introduced to remedy the traditional DEA's deficiency that it totally depends on input and output index data and cannot reflect the importance of real index. Through the practical use in a city of Jiangsu Province, the Synthetic Evaluation Method of DEA/AHP is suitable and operable in dealing with MRT system's decision making process.

In this paper, a method called Ticket Survey Method from which the bus station origin and destination (OD) can easily and exactly be got was proposed, then combined with the characteristic of the Ticket Survey Method, the bus station OD can be obtained with Visual Basic program language, lastly by use of the attachment relationship between plots and bus stations, assignment weight coefficient was introduced and the relationship of assignment weight was established, then the transit OD matrix can be estimated from bus station OD. There is no necessary for massive iterative computation. In this way, the new model may be applied to large-scale transportation network problems.

Accessibility is a fundamental concept in transportation analysis and planning. Typically, accessibility refers to the ‘ease’ of reaching opportunities for activities and services can be used to evaluate the performance of a transportation system. In this paper, Utility-Based (UB) accessibility measures derived from the combined travel demand model are used to assess the performance of public transport network. Network-based accessibility measures using a combined travel demand model that is consistent with random utility theory are established. By using travel time and ticket fees via bus as the measures, accessibility between any two bus stops is described from the user’s perspective. An example is provided in this paper to examine the effect of this method. The results indicate that the accessibility measures proposed in this paper are capable of measuring the degree of bus stop accessibility to assess the public service.

Analyzing and appraising the operational situation of bus lines is an important basis on which bus companies can adjust bus lines and establish operation schedules. However, at present, the assessment of bus lines lacks accurate and scientific methods. This article proposed to use a Fuzzy C-means clustering algorithm to comprehensively analyze the structural difference and operational effects of bus lines. By comparing the evaluation indexes of each line, those with similar structure and operational effects were classified into one category, each with its own unique structural and operational characteristics. Based on this, the algorithm was applied to the operational situation of the public transit system in Changzhou city. As a result of the evaluation process, suggestions to optimize the transit system are proposed.

The aim of this paper is to analyze the influence of speed dispersion on traffic safety. The average speed difference (ASD for short) of two neighboring vehicles was used to define the speed dispersion. Based on the traffic data obtained from Airport freeway in China and A20 freeway in the Netherlands, we carried out a comparative study on speed dispersion characteristics. We found that the speed dispersion of traffic flow on Airport freeway were larger than that on A20 freeway. The comparing results showed that A20 freeway was safer than Airport freeway. Some possible reasons were provided in this paper. This indicates that the ratio of large vehicle, traffic flow and over-speed are the three main factors of speed dispersion influence on traffic safety.

Large-scale uniform ternary ZnIn2S4 nanosheet array films have been grown on indium-tin oxide (ITO) glass substrates effectively by a simple direct elemental reaction route. Inorganic–organic bulk heterojunction photovoltaic devices were fabricated with ZnIn2S4 film as an electron acceptor and poly (2-methoxy-5-(2/-ethylhexoxy)-1,4-phenylene vinylene (MEH-PPV) polymer as electron donor. The formation of the ZnIn2S4 nanosheet film was characterized by X-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscopy (SEM) and UV-VIS-NIR spectroscopy. The as-prepared ZnIn2S4 nanosheet film was demonstrated as an n-type semiconductor exhibiting an apparent photocurrent response and good photostability. The photovoltaic performance of the constructed solar cell device with the active structure of ITO/ZnIn2S4:MEH-PPV/Au was evaluated at room temperature under AM1.5G (100 mW cm−2) simulated globe sun illumination from a filtered Xenon lamp.

Activity-travel behavior differs between women and men. Previous researches dealing with gender differences mainly focus on travel in itself rather than the interaction between time-use pattern and travel mode choice. Based on the activity-travel survey data of Suzhou, China, multigroup structural equation modeling is adopted to explore the reason for gender-based differences in mode choice through comparing the interactions among sociodemographics, time-use pattern, and mode choice. The results indicate that gender-based differences do exist in mode choice. Women prefer traveling by bicycle while men prefer traveling by car in Suzhou. And compared to women, men's mode choice is not so easy to be affected by other travel modes. Besides, gender-based differences exist in the magnitude or the sign of the interrelations among socio-demographics, time-use pattern, and mode choice. It is better to explain gender-based differences in mode choice by including time-use pattern endogenously than through socio-demographics alone. Furthermore, the study shows that by examining the direct, indirect, and total effects in the model system simultaneously, we are able to better capture the differences in mode choice across genders and further able to understand the reason for those differences. Finally, some dedicated suggestions are presented for planners and government to ensure a healthy transportation system.

The wide use of smart card automatic fare collection (AFC) systems in public transit makes it popular to analyse public transit user behaviour based on smart card data. Most smart-card-based researches are time-related, but smart card data time is not always reliable because smart card systems are usually off-line and lack of maintenance in China, and smart card data of different buses often shares different time references. This paper explores the application of dynamic time warping to unify time reference of smart card data. Based on the analysis of the relationship between boarding time in smart card data and arrival-departure time in automatic vehicle location (AVL) data, a smart card data time reference unification algorithm was proposed. The results indicate that the algorithm can work out reliable time offsets by numerical calculation and runs well with integral automatic vehicle location data. Due to the fact that the time reference of smart card data for each bus changes daily, the best way to put an end to smart card time reference problem is to make smart card systems online, such as combining smart card readers with automatic vehicle location systems.

Public transit information can help people make trip decisions. Requirements of public transit information in China were surveyed in this dissertation. Current status of using public transit information, approaches of obtaining information are the focuses in web-survey. The preferences of passengers using public transit information were analyzed and the opinions on existing public transit information service from passengers were also detailed. The conclusions of this survey demonstrated some characteristics of passengers using public transit information in China, and provided the basis for future studies.