Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

Adverse weather has a considerable impact on the behavior of drivers,which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents.This research examines how drivers'perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment.An expressway road scenario was built in a driving simulator.Eleven types of weather conditions,including clear sky,four levels of fog,four levels of rain and two levels of snow,were designed.Furthermore,to simulate the carfollowing behavior,three car-following situations were designed according to the motion of the lead car.Seven car-following indicators were extracted based on risk homeostasis theory.Then,the entropy weight method was used to integrate the selected indicators into an index to represent the drivers'perceived risk.Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk,and the coefficients were considered as indicators.The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior.Drivers'perceived risk tends to increase with the worsening weather conditions.Under conditions of extremely poor visibility,such as heavy dense fog,the measured drivers'perceived risk is low due to the difficulties in vehicle operation and limited visibility.

The impact of carbon emission trading policy on firms’green innovation in China

This study aims to examine the green innovation effect of the carbon emissions pilot policy in China.First,using the difference-in-differences method and regressions of instrumental variables using the data from Chinese listed firms,we verify that the policy promotes green innovation among regulated firms and is more pronounced among state-owned enterprises,firms in the eastern region,and those with lower financing constraints.Furthermore,this positive effect spreads downstream relative to the regulated firms through input–output linkages,but reduces green innovation to upstream firms.Accordingly,such diffusion of innovation is achieved through the price mechanism.The results necessitate the introduction of various derivatives to mobilize the market to reduce the speculative volatility of carbon prices.In addition,relevant supporting policies must be established to encourage corporate innovation to reduce the crowding-out effect owing to emission reduction and the nonmarket factors.

CONSENSUS ANALYSIS AND DESIGN OF LINEAR INTERCONNECTED MULTI-AGENT SYSTEMS

We deal with the state consensus problem of a general Linear Interconnected Multi-Agent System （LIMAS） under a time-invariant and directed communication topology. Firstly, we propose a linear consensus protocol in a general form, which consists of state feedback of the agent itself and feedback form of the relative states between the agent and its neighbors. Secondly, a state-linear-transformation is applied to equivalently transform the state consensus problem into a partial stability problem. Based on the partial stability theory, we derive a sufficient and necessary criterion of consensus convergence, which is expressed via the Hurwitz stability of a real matrix constructed from the parameters of the agent models and the protocols, and present an analytical formula of the consensus function. Lastly, we propose a design procedure of the gain matrices in the protocol by solving a bilinear matrix inequality.

The walking distance decay law of amenity selection based on binary logistic model

The current measuring methods of walkability,such as the walk score,consider that walking distance decay laws for all amenities are the same,which is not applicable to typical communities in China with plentiful resources.Therefore,the walking distance decay laws of multi-type and multi-scale facilities are studied.Firstly,based on the residents'amenity selection survey,the walking distance decay law of residents'choice of amenity was studied from three aspects,including the law of all amenities,the laws of different types of amenities and the laws of different scales of amenities.It was proved that the walking distance decay laws of different kinds of amenities showed a significant difference.Secondly,different amenities'acceptable walking distance and optimum walking distance were obtained according to previous studies and the decay curve.Amenities with higher attraction and/or a larger scale showed a longer acceptable walking distance and optimum walking distance.Finally,the binary logistic model was used to describe the relationships between walking distance,amenity type,amenity scale and the probability of one amenity being selected,the prediction accuracy of which reached 80.4%.The calculated probability obtained from the model can be used as the decay coefficient of amenities in the measurement of walkability,providing a reference for the site selection and evaluation of amenities.

Study on Objectives and Strategies for Energy Conservation and Emission Reduction in Transportation Industry during the 13th Five-Year Plan Period in Beijing

Facing increasing passenger and cargo transport demand and limited re-source in the 13th Five-Year period, how to make a breakthrough and substantial progress has become a key issue on planning and the Top-level Design. In this paper we judged and analyzed the current development and potential demand of the energy saving and emission reduction in Beijing traffic industry. Through application of energy and emission prediction model which based on the vehicle activity data, the development goals of “one drop, double control, and triple upgrade” have been put forward. In order to achieve the goal, “5 + 1” development strategies should be implemented, and we also proposed the thinking and recommendations on sustainable development of transportation.

PARTIAL STABILITY ANALYSIS OF SOME CLASSES OF NONLINEAR SYSTEMS

A nonlinear differential equation system with nonlinearities of a sector type is studied. Using the Lyapunov direct method and the comparison method, conditions are derived under which the zero solution of the system is stable with respect to all variables and asymptotically stable with respect to a part of variables. Moreover, the impact of nonstationary perturbations with zero mean values on the stability of the zero solution is investigated. In addition, the corresponding time-delay system is considered for which delay-independent partial asymptotic stability conditions are found. Three examples are presented to demonstrate effectiveness of the obtained results.

Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data

Pedestrian safety has recently been considered as one of the most serious issues in the research of traffic safety. This study aims at analyzing the spatial correlation between the frequency of pedestrian crashes and various predictor variables based on open source point-of-interest (POI) data which can provide specific land use features and user characteristics. Spatial regression models were developed at Traffic Analysis Zone (TAZ) level using 10,333 pedestrian crash records within the Fifth Ring of Beijing in 2015. Several spatial econometrics approaches were used to examine the spatial autocorrelation in crash count per TAZ, and the spatial heterogeneity was investigated by a geographically weighted regression model. The results showed that spatial error model performed better than other two spatial models and a traditional ordinary least squares model. Specifically, bus stops, hospitals, pharmacies, restaurants, and office buildings had positive impacts on pedestrian crashes, while hotels were negatively associated with the occurrence of pedestrian crashes. In addition, it was proven that there was a significant sign of localization effects for different POIs. Depending on these findings, lots of recommendations and countermeasures can be proposed to better improve the traffic safety for pedestrians.

Impact of Pedestrian Traffic on Saturation Rate of Protected Left-Turn at Urban Intersections

Heavily congested intersections in metropolitan areas in China are facing unique problems due to high travel demand and a high degree of traffic law violations. Based on a study conducted by the authors of this paper, 93% of left-turn vehicles turning left in these areas were slowed in order to avoid conflict with pedestrians. Intertwined pedestrian and vehicular flows can significantly reduce the capacity of exclusive left-turn lane group through reducing saturation flow rate, which increases the congestion at intersections. This paper investigates how the saturation flow rate of exclusive left-turn lane group is affected by the characteristics of pedestrian flow. By analyzing the imagery data collected by video cameras installed at intersections, the research team is able to obtain the characteristics of both vehicular and pedestrian flows, such as speed and spatial locations. The average operating speed at the saturation flow rate with and without pedestrian traffic is used as a direct measurement to evaluate the effect of pedestrians. Based on the statistical analysis, the paper concludes that saturation flow rate is mainly affected by the position of pedestrian in the crosswalk (inside or outside of left-turn vehicle’s trajectory), and the distance between the vehicle and pedestrians. In general, when the distance is less than four meters, the smaller the distance between vehicle and pedestrians, the larger the impact. However, there is no significant impact when the distance is larger than four meters. To accurately quantify the effect, the degree of pedestrian-vehicle impact is defined in four levels. The results show that the difference in the saturation flow rate between the best and the worst level could be 15.7%, which clearly indicates how important it is to enforce pedestrian crossing behavior.

Bicycle Level of Service Evaluation Method for Urban Road Segment

As a sustainable mode of transportation, bicycles significantly improve daily mobility. In order to provide theoretics support for improvement of the bicycling environment, this paper proposed bicycle level of service (BLOS) evaluation method for urban road segment according to cyclists’ perception. First, influence factors of BLOS were identified from aspects of road facility, traffic characteristics, and environmental condition. Second, bicycling videos were recorded and a satisfaction survey was conducted. Four BLOS evaluation models for different separation facilities were established. Last, bicycling behavioral stages of travelers were divided based on the transtheoretical model. A new BLOS classification criterion was proposed according to travelers’ demand of different stages.

Distribution Prediction Correction Based on the Analysis of Job/Housing Balance between Old and New Districts

The cities of China are in the period of rapid development of urbanization. The practical problems, which faced by urban traffic planning, include an effective prediction for the traffic volume. This paper, combining the example of Cangzhou’s traffic planning, analyzes the differences on land use between the new and the old district, including its influence to the residents. It also analyzes the relationship of travel features and the coefficient of job/housing balance in the new and the old district;furthermore, it corrects the result of traditional gravity model by using balance’s coefficient. Through this study, we find that the corrected model deals with the problem of travel forecasting’s deviation, which is closer to the physic truth. In the meanwhile, we suggested paying more attention to the relationship of land use and housing/jobs in the district plan, in this way, it will reduce the traffic demand radically and optimize the traffic structure.

Application of Access Management Techniques to Large Shopping Mall in Beijing

Conflicts are common phenomena in the field of traffic engineering, while are also the main cause of all the traffic problems. The vehicle access driveway of large shopping mall is often the conflict area of urban roads, traffic engineers and city managers face a great challenge as to make the traffic enter and exit the shopping mall safely and or-derly. This paper studies the issues of access management of large shopping mall with a focus on Carrefour shopping mall in Shuangjin district, Beijing. This shopping mall attracts and generates lots of traffic during peak hours, those huge numbers of vehicles come in and out of the shopping mall through the access driveway, resulting in a lot of conflicts to the urban road traffic flow and deteriorating highly smooth movement of traffic on the urban roadway. The impacts of the existing design and layout of access driveway on surrounding traffic operation are also studied in this paper. Some countermeasure of improvements had proposed to decrease traffic conflicts and make sure that the traffic conditions get better off, all the improvement countermeasures are based on the access management principles. Results from this study can be a good reference on other similar driveways at large shopping mall areas.

Area Classification of Surrounding Parking Facility Based on Land Use Functionality

The different land use surrounding parking facility has significant impact on parking behavior. This paper studies the functional classification of land use surrounding parking facility, which is fundamentally important for indepth research on parking behavior. 37 parking facilities located between the second and sixth ring roadway in Beijing were selected for this study. Based on the surveys conducted at these parking facilities, various parking behavior were analyzed, based on which the scope of the different parking was determined. The information on location, land use characteristics, public transport, the surrounding parking situations are collected for each investigated parking facility. Applying the SPSS clustering method, the threshold was developed for the classification. Totally, five categories are proposed for the land use functionality surrounding parking facility as the results of this study.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

A new cellular automaton for signal controlled traffic flow based on driving behaviors

The complexity of signal controlled traffic largely stems from the various driving behaviors developed in response to the traffic signal. However, the existing models take a few driving behaviors into account and consequently the traffic dynamics has not been completely explored. Therefore, a new cellular automaton model, which incorporates the driving behaviors typically manifesting during the different stages when the vehicles are moving toward a traffic light, is proposed in this paper. Numerical simulations have demonstrated that the proposed model can produce the spontaneous traffic breakdown and the dissolution of the over-saturated traffic phenomena. Furthermore, the simulation results indicate that the slow-to-start behavior and the inch-forward behavior can foster the traffic breakdown. Particularly, it has been discovered that the over-saturated traffic can be revised to be an under-saturated state when the slow-down behavior is activated after the spontaneous breakdown. Finally, the contributions of the driving behaviors on the traffic breakdown have been examined.

Tri-level programming model for combined urban traffic signal control and traffic flow guidance

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.

Three-stage approach for dynamic traffic temporal-spatial model

In order to describe the characteristics of dynamic traffic flow and improve the robustness of its multiple applications, a dynamic traffic temporal-spatial model(DTTS) is established. With consideration of the temporal correlation, spatial correlation and historical correlation, a basic DTTS model is built. And a three-stage approach is put forward for the simplification and calibration of the basic DTTS model. Through critical sections pre-selection and critical time pre-selection, the first stage reduces the variable number of the basic DTTS model. In the second stage, variable coefficient calibration is implemented based on basic model simplification and stepwise regression analysis. Aimed at dynamic noise estimation, the characteristics of noise are summarized and an extreme learning machine is presented in the third stage. A case study based on a real-world road network in Beijing, China, is carried out to test the efficiency and applicability of proposed DTTS model and the three-stage approach.

A Model for Capacity Considering the Interference by Pedestrian Traffic at Signal Intersections

The capacity is impacted badly by pedestrians' violation behavior at signal intersections. In order to quantify the impact,the time-headway and the vehicular speed from start-up to reach the stable saturation flow with and without pedestrian traffic are used as the direct measurements. Using the statistical analysis,the timeheadway of saturation flow is mainly affected by the position of pedestrians and the degree of pedestrians 'influence is classified into four levels. Then the speed-time profile for the vehicular acceleration at each level is fitted by Curve Fitting Software. Based on the effect to the time-headway and acceleration time,the model of capacity influenced by pedestrians is established and the influence is quantified, which enriches the fundamental theory of traffic engineering. The result shows that the vehicular capacity can be decreased by 14%at the worst case( level I). The conclusions obtained in this paper are valuable for better management of the signal intersection.

Analyzing Pedestrian Fatality Risk in Accidents at Mid-Blocks

Objective: Pedestrian safety is considered as one of the greatest concerns, especially for developing countries. In the year of 2015, about 48% pedestrian accidents with 56% fatalities occurred at mid-blocks in Beijing. Since the high frequency and fatality risk, this study focused on pedestrian accidents taking place at mid-blocks and aimed at identifying significant factors. Methods: Based on total 10,948 crash records, a binary logit model was established to explore the impact of various factors on the probability of pedestrian’s death. Furthermore, first-degree interaction effects were introduced into the basic model. The Hosmer-Lemeshow goodness-of-fit test was used to assess the model performance. Odds ratio was calculated for categorical variables to compare significant accident conditions with the conference level. Variables within consideration in this study included weather, area type, road type, speed limit, pedestrian location, lighting condition, vehicle type, pedestrian gender and pedestrian age. Results: The calibration results of the model show that the increased fatality chances of an accident at mid-blocks are associated with normal weather, rural area, two-way divided road, crossing elsewhere in carriageway, darkness (especially for no street lighting), light vehicle, large vehicle and male pedestrian. With road speed limit increasing by 10 km/h, the probability of death accordingly increases by 46%. Older victims have higher chances of being killed in a crash. Moreover, three interaction effects are found significant: rural area and two-way divided, rural area and crossing elsewhere as well as speed limit and pedestrian age. Conclusions: This study has analyzed police accident data and identified factors significant to the death probability of pedestrians in accidents occurred at mid-blocks. Recommendations and improving measures were proposed correspondingly. Behaviors of different road users at mid-blocks should be taken into account in the future research.

Evaluating R&D efficiency of China's listed lithium battery enterprises

Promoting the growth of the lithium battery sector has been a critical aspect of China's energy policy in terms of achieving carbon neutrality.However,despite significant support on research and development(R&D)investments that have resulted in increasing size,the sector seems to be falling behind in technological areas.To guide future policies and understand proper ways of promoting R&D efficiency,we looked into the lithium battery industry of China.Specifically,data envelopment analysis(DEA)was used as the primary approach based on evidence from 22 listed lithium battery enterprises.The performance of the five leading players was compared with that of the industry as a whole.Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019.However,during this period,a significant increase in R&D expenditure was witnessed.This finding was supported,as the results showed that the average technical efficiency of the 22 enterprises was 0.442,whereas the average pure technical efficiency was at 0.503,thus suggesting that they were suffering from decreasing returns to scale(DRS).In contrast,the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry's average.Moreover,they were experiencing increasing scale efficiency(IRS).We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale;rather,it should also encompass assisting businesses in developing efficient managerial processes for R&D.

Data-Driven Tight Frame for Multi-channel Images and Its Application to Joint Color-Depth Image Reconstruction

In image restoration,we usually assume that the underlying image has a good sparse approximation under a certain system.Wavelet tight frame system has been proven to be such an efficient system to sparsely approximate piecewise smooth images.Thus,it has been widely used in many practical image restoration problems.However,images from different scenarios are so diverse that no static wavelet tight frame system can sparsely approximate all of themwell.To overcome this,recently,Cai et.al.(Appl Comput Harmon Anal 37:89–105,2014)proposed a method that derives a data-driven tight frame adapted to the specific input image,leading to a better sparse approximation.The data-driven tight frame has been applied successfully to image denoising and CT image reconstruction.In this paper,we extend this data-driven tight frame construction method to multi-channel images.We construct a discrete tight frame system for each channel and assume their sparse coefficients have a joint sparsity.The multi-channel data-driven tight frame construction scheme is applied to joint color and depth image reconstruction.Experimental results show that the proposed approach has a better performance than state-of-the-art joint color and depth image reconstruction approaches.