Trip Probability Research Articles

Freight trip distribution using spatiotemporal aggregate data: A modified collective flow diffusion model-based approach

The estimation of freight trip distribution is an important issue in freight transport studies, and collecting data on the spatiotemporal aggregated number of freight vehicles without trip information is relatively easy and inexpensive. In this paper, we propose a new model for freight distribution estimation, referred to as “TripCFDM.” Unlike the traditional collective flow diffusion model (CFDM), which solely relies on aggregate data, TripCFDM relies on aggregate data along with a small amount of trip data. CFDM estimates the probability of latent zonal freight trips based on aggregate data corresponding to the number of vehicles in each zone and time step. The use of the aggregate and small amounts of trip data ensures a realistic data acquisition environment. Our model is applied to freight trips in the Tokyo Metropolitan Area using the National Survey of Roads and Streets Traffic Conditions (Road Traffic Census). The results reveal that TripCFDM yields a significant improvement in the estimated probability compared with CFDM, particularly for intrazonal freight trips. However, the estimation results of interzonal trips obtained by CFDM and TripCFDM tend to be similar, suggesting that CFDM alone may be adequate to estimate the probability of interzonal trips with a sufficient degree of accuracy; nevertheless, TripCFDM is a better method for freight trip distribution estimation, including for intrazonal trips. The results of CFDM are not significantly affected by the time steps in the input data, whereas TripCFDM can yield higher accuracies with longer time steps. Regarding the number of zones and zone size, sufficient zone aggregation may be effective in improving the estimation accuracy because a detailed zonal division leads to a small probability of origin–destination trips with extensive computations. Based on this modified CFDM approach, origin and destination matrices for freight transport can be generated without large-scale surveys.

Generalized estimation method of tripping probability for sensitive equipment under voltage sags based on split H blocks

Generalized estimation method of tripping probability for sensitive equipment under voltage sags based on split H blocks

Walking accessibility to non-work facilities and travel patterns in suburban new towns

Previous research on the self-containment of new towns has focused on access to employment and commute travel; little research has been documented regarding access to non-work facilities and its relation to non-work active travel, overlooking an important aspect to assessing the self-containment of new towns. Drawing on multiple sources of data in Hong Kong, we measure walking accessibility to three main types of non-work facilities (markets, restaurants and parks) as major destinations for non-work travel. Through a series of statistical analysis, we investigate in detail the effects of accessibility on the probability and duration of home-based walking trips. Our findings indicate that: (1) new towns were more disadvantaged and less equitable in terms of the accessibility to non-work facilities than urban areas; and (2) accessibility increased both the likelihood and duration of new town residents' walking trips particularly for grocery-shopping and dining-out purposes. The findings indicate that enhancing accessibility to non-work facilities in new towns can be beneficial in terms of both achieving self-containment and promoting more sustainable travel behaviour. Recommendations are proposed accordingly to help create more pedestrian-friendly environment, and balanced provision of non-work facilities in and around new towns and other suburban developments.

Effects of prolonged brisk walking induced lower limb muscle fatigue on the changes of gait parameters in older adults

BackgroundLower extremity muscle fatigue affects gait stability and increases the probability of injuries in the elderly. Research questionHow does prolonged walking-induced fatigue affect lower limb muscle activity, plantar pressure distribution, and tripping risk? MethodsEighteen elderly adults walked fast on a treadmill for 60 minutes at a fixed speed. The plantar pressure was measured with an in-shoe monitoring system, eight lower limb muscles were monitored using surface electromyography, and foot movements were tracked by a motion capture analysis system. The above data and participants’ subjective fatigue level feedback were collected every 5 minutes. Statistical analysis used the Friedman one-way repeated measures analysis of variance by ranks test followed by Wilcoxon signed-ranks test with Benjamini-Hochberg stepwise correction. ResultsThe subjective reported fatigue on the Borg scale increased gradually from 1 to 6 (p = 0.001) during the 60 minutes, while the EMG amplitude of vastus medialis significant decreased (p = 0.013). The results of plantar pressure demonstrated that the distribution of load and impulse shifted medially in both the heel and arch regions while shifted laterally in both the toes and metatarsal regions. The significantly increased contact area supports this shift at the medial arch (p = 0.036, increased by 6.94%, the 60th minute vs. the baseline). The symmetry of medial-lateral plantar force increased at the toes, metatarsal, and arch regions. The significantly increased parameters also include the swing time and contact time. The minimum foot clearance was reduced, increasing tripping probability, not significantly, though. SignificanceThis study facilitates a better understanding of changes in lower limb muscle activity and gait parameters during prolonged fast walking. Besides, this study has good guiding significance for developing smart devices based on plantar force, inertial measurement units, and EMG sensors to monitor changes in muscle activation in real-time and prevent tripping.

A Low-Data-Dependence Assessment Method of Industrial Process Interruption Probability Due to Voltage Sag

Information about the interruption probability of sensitive industrial processes is important for devising mitigation strategies against voltage sags. However, the existing method for assessing interruption probability is less practical, owing to the difficulty in collecting technical information and data from industrial users. This study proposes a low-data-dependence assessment method for the interruption probability of an industrial process. First, it establishes a general assessment model for the tripping probability of sensitive equipment. Second, it proposes a parameter estimation method to determine the probability density function of the voltage tolerance curve of sensitive equipment in the uncertainty region, according to the equipment trip information recorded by the user. This improves the accuracy of the assessment. Third, this study establishes the logic function expression form of the industrial process structure to describe the relationship between the process interruption probability and the equipment tripping probability. Finally, it proposes a solution method for the process interruption probability based on a heuristic search algorithm, which overcomes the problem of technological process information acquisition, such as the logical relationship among various equipment in the industrial process. The rationality and applicability of the proposed method are verified through a case study.

Premium power supply scheme based on leasing mitigation equipment against voltage sag

Small and medium-sized sensitive users are eager to have premium power supply. The common solution is purchasing mitigation equipment (ME). However, some of they have the financial pressure that cannot afford the mitigation. In order to solve this problem, this paper proposes a premium power supply scheme based on leasing mitigation equipment against voltage sag (PSLM). First, the scheme of PSLM is proposed, which consists of stakeholders, leasing modes and leased property. Second, this paper presents an algorithm of sensitive equipment (SE) trip probability considering effect of ME, to calculate trip probability of SE when it is equipped with ME, which reflects the mitigation effect that is related to profits of scheme. Then technical parameters of ME are optimized by an optimal configuration model, considering tolerance of SE under voltage sag. The cost of the scheme is determined by the optimization of technical parameters of ME. Furthermore, this paper proposes a calculation model of rental of PSLM, to maximize the revenues of both stakeholders. Non-dominated sorting genetic algorithm-II is used to solve the proposed two models. Finally, the effectiveness and feasibility of the proposed PSLM is verified by a case study of a grain oil manufacturer in the south China.

Solution of the emergency control of synchronous generator modes based on the local measurements to ensure the dynamic stability

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre-defined data. The purpose of this study is to develop an adaptable algorithm for turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator “torque–load angle”. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre-defined parameters of a power system model are required. The suggested algorithm was tested on the power system model NE39bus using Matlab/Simulink. The efficiency of the suggested algorithm is verified and demonstrated.

Application of the Conditional Optimization Method for the Synthesis of the Law of Emergency Control of a Synchronous Generator Steam Turbine Operating in a Complex-Closed Configuration Power System

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre-defined data. The purpose of this study is to develop an adaptable algorithm of turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator Torque—Load angle. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre-defined parameters of a power system model are required.

Adopting Differential Lightning Protection Measures to Reduce Simultaneous Trip Probability of 220 kV Double-Circuit Transmission Line on the Same Tower

Double-circuit lines on the same tower are more widely used due to the increasing shortage of transmission corridors. If lightning strikes cause simultaneous tripping, it will cause severe economic losses and safety problems. Transmission lines with 220 kV voltage level are an essential part of the main grid. Reducing the simultaneous tripping rate of 220 kV lines is extremely important for the safe operation of the power grid. This paper analyses the advantages and disadvantages of several typical lightning protection methods for double-circuit lines on the same tower by simulation calculation. Combined with the characteristics of several typical double-circuit towers, lightning protection design for double-circuit lines is studied, and the differentiated lightning protection methods and basic protection processes for typical lines are proposed.

Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults’ foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations.

Optimal Operation and Control Design of DC Arrester for Renewable Energy Transmission

The trip probability of transmission line under lightning stroke is rapidly increasing under complex weather conditions when large-scale renewable energy is intergrated with modern power grid. Line arrester plays a critical role in reducing lightning damage and economic losses, in which DC reference voltage is a critical parameter to evaluate line arresters. Hence, a set of on-site DC test system of transmission line arresters is developed to realize the DC reference voltage test in this article. The output DC voltage of the test system is continuously adjustable (0–200 kV), while its rated power and the maximum output current are set to 400 W and 2 mA, respectively. Furthermore, the system is powered by a lithium battery instead of a 220/380 V AC energy source. It employs split design and integrating assembly on-site that reduces the total weight by 36.7%, which considerably decreases the demand for the space of test site. Besides, the weights of control cabinet, multiplying cylinder, and energy source are reduced by 60, 54.55, and 33.33%, respectively. Meanwhile, the Bluetooth remote control module is used to effectively ensure the safety of test personnel. This system accomplishes DC reference voltage tests without removing line arresters on site, which can effectively enhance maintenance efficiency and economic benefits.

A Scoping Review on Minimum Foot Clearance Measurement: Sensing Modalities.

Background: Falls are a major public health issue and tripping is the most common self-reported cause of outdoor falls. Minimum foot clearance (MFC) is a key parameter for identifying the probability of tripping. Optical motion capture systems are commonly used to measure MFC values; however, there is a need to identify alternative modalities that are better suited to collecting data in real-world settings. Objective: This is the first of a two-part scoping review. The objective of this paper is to identify and evaluate alternative measurement modalities to optical motion capture systems for measuring level-ground MFC. A companion paper identifies conditions that impact MFC and the range of MFC values individuals that these conditions exhibit. Methods: We searched four electronic databases, where peer-reviewed journals and conference papers reporting level-ground MFC characteristics were identified. The papers were screened by two independent reviewers for inclusion. The reporting was done in keeping with the PRISMA-ScR reporting guidelines. Results: From an initial search of 1571 papers, 17 papers were included in this paper. The identified technologies were inertial measurement units (IMUs) (n = 10), ultrasonic sensors (n = 2), infrared sensors (IR) (n = 2), optical proximity sensors (OPS) (n = 1), laser ranging sensors (n = 1), and ultra-wideband sensors (n = 1). From the papers, we extracted the sensor type, the analysis methods, the properties of the proposed system, and its accuracy and validation methods. Conclusions: The two most commonly used alternative modalities were IMUs and OPS. There was a lack of standardization among studies utilizing the same measurement modalities, as well as discrepancies in the methods used to assess performance. We provide a list of recommendations for future work to allow for more meaningful comparison between modalities as well as future research directions.

Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO

This paper studies a two-stage model for the optimization of charging scheduling and charging station construction planning based on the spatial-temporal complementarity of charging demand, in order to improve the overall satisfaction of the whole society. In the first stage, starting from the probability of trip starting point and other characteristics, the simulation of load demand is realized based on Monte Carlo simulation, and the charging plan regulation method is created combined with the principle of Dijkstra algorithm. In the second stage, a solution path based on discrete binary particle swarm optimization is proposed. The experimental results of 14 scenarios show that no matter disorderly charging or orderly charging, the annual social comprehensive cost of the scenario with five charging stations is the lowest. Compared with disordered charging, the total construction capacity of charging station is reduced by 51.61%, and the annual social comprehensive cost is reduced by 29.35%. Therefore, this study provides a framework for the optimal configuration of charging stations, which is applicable when considering or not considering demand response, and provides a solution for the future green development planning of urban charging infrastructure.

Ride-hailing and taxi versus walking: Long term forecasts and implications from large-scale behavioral data

IntroductionAlthough ride-hailing and taxi trips can potentially reduce single-occupant vehicle trips and auto ownership, they can also replace pedestrian trips. Because physical activity is associated with improved health outcomes, the extent to which ride-hailing and taxi travel captures walking's mode share is of interest to policymakers. MethodsBased on large-scale behavioral data from the 2017 U.S. National Household Travel Survey, this paper reports on the development of a full Bayesian logistic regression model for determining the mode split between (1) ride-hailing and taxi and (2) walk while accounting for unobserved heterogeneity. The results from the stand-alone model inform two longer-term travel forecasting scenarios: a) higher risk of walk trips converting to ride-hailing and taxi, specifically in the future with high prevalence of automated vehicles, b) higher probability of such trips remaining as walking. ResultsThe results revealed that some of the important characteristics that increase the likelihood of a traveler using the ride-hailing and taxi mode versus walking include having a longer trip, using a smartphone to access the internet, having an interest in technologies, having a medical condition, and living in a metropolitan area with rail access. Further, the results from the first scenario suggest that an overall increase of up to 2.9% in the ride-hailing and taxi mode share may be expected. The second scenario shows that between 68% and 76% of ride-hailing and taxi trips could be diverted to walking if supportive pedestrian infrastructure were provided in the case study locations. The planning process can be adapted to consider not only congestion, crash, and emissions impacts of such shifts but also the effects of a loss of physical activity. ConclusionsThe study findings show how the ride-hailing and taxi mode competes with walking. Further, the findings enable planners to update their regional travel forecasting models; policy makers can thus encourage active travel by prioritizing pedestrian infrastructure investments that may divert ride-hailing and taxi trips to walking. However, equity should be a key consideration to ensure that addressing the competition between these two modal choices does not hinder the provision of pedestrian facilities in communities that depend on walking.

Understanding non-commuting travel demand of car commuters – Insights from ANPR trip chain data in Cambridge

Abstract The paper investigates the non-commuting travel demand of car commuters using Automatic Number Plate Recognition (ANPR) trip chain data in Cambridge, UK. A novel rule-based algorithm is developed for identifying commuting vehicles and the associated non-commuting trips. Identification results are validated with external data. Non-commuting travel demand is investigated in terms of trip probability, average trip frequency, duration and demand elasticity. The study finds that, first, non-commuting trips represent a significant source of travel demand for car commuters – car commuters who engage in non-commuting activities in their daily trip chains would on average spend approximately 2.7hr on those activities including travel time on a typical workday in Cambridge. Second, longer working hours are associated with a lower probability of engaging in non-commuting trips, implying a substitution effect within the daily travel time budget. Last, in terms of travel demand elasticity, non-commuting trips starting in the early morning (6–9am) are less elastic than those starting in the morning (9–12am) and during the lunch break (12-3pm). The varying demand elasticities are likely to be attributed to the different travel constraints associated with certain trip purposes. Implications for post-pandemic traffic demand and management are drawn.

Risk warning technology for the whole process of overhead transmission line trip caused by wildfire

Wildfire disasters on overhead transmission lines seriously threaten the safe and stable operation of large power grids and the normal use of electricity. After a wildfire occurs near a transmission line, it is often inefficient to take measures afterward. In order to guide the early warning of wildfire disasters on overhead transmission lines and strengthen the active prevention of them, this paper proposes a method for calculating the occurrence risk of overhead transmission line wildfires that considers the hazards of vegetation burning. Compared with conventional methods, the considerations are more comprehensive. A calculation method for wildfire trip risk coefficient based on tripping probability and transmission load that does not need to calculate the wildfire spread process is proposed, and the calculation is simpler. For the first time, a comprehensive calculation method for the whole-process tripping risk of overhead transmission lines caused by wildfires combined with the wildfire occurrence risk and the trip risk coefficient is proposed, compared with methods that calculated only the risk level of wildfires and ones that calculated only the tripping probability, comprehensively analyzing the entire process from the potential factors of wildfires to the danger of tripping, which is more scientific. Application cases showed that this method accurately reflected the risk of wildfire trips on overhead transmission lines, thereby guiding the optimization of the wildfire prevention resource deployment in advance and improving wildfire prevention in power grids.

Calculation of specific operating modes of relay protection for AC traction networks

Relay protection of traction networks against short-circuit currents in alternating current sections should have both selectivity and speed. However, in areas with an increased probability of burnout of contact wires during a short circuit, selectivity is often put away to increase the speed of response. Such operating modes of relay protection in the traction network are specific. The author considers one of these modes, namely, the operation of distance protections with an increased step area without time delay with a partially non-selective protection system, which ensures the fastest possible shutdown of damage within the entire protected area. At the same time, it is indicated that an increase in the coverage area of a step without a time delay to the size of the protected area can cause non-selective tripping of a number of switches in case of damage near substations or a sectioning post. Therefore, it is proposed to select the optimal value of the tunable zone of non-selective protection action, at which the probability of non-selective tripping of switches will be minimal. For this, the author presents an analytical solution to this problem, which allows automating calculations when forming proposals for setting up protection in a specific operating mode. It is noted that by choosing the sensitivity coefficient value in the formulas, it is possible to adjust the zone of non-selective action of distance protections so that its real value will approach the size of the protected area, while remaining strictly larger than it, which will eliminate the danger of burnout of contact wires in case of a short circuit and reduce the probability non-selective disconnection of switches on the site.

Minimum toe clearance and tripping probability in people with unilateral transtibial amputation walking on ramps with different prosthetic designs

BackgroundMinimum Toe Clearance (MTC) is defined as the minimum vertical distance between the lowest point under the front part of the foot and the ground, during mid-swing. Low values of MTC and walking on inclines are both related to higher probability of tripping and falling in lower limb amputees. New prosthetic designs aim at improving MTC, especially on ramps, however the real effect on MTC still needs investigation. The objective of this study was then to evaluate the effect of different prosthetic designs on MTC in inclined walking. MethodsThirteen transtibial amputees walked on a ramp using three different prostheses: non articulating ankle (NAA), articulating hydraulic ankle (AHA), and articulating hydraulic ankle with microprocessor (AHA-MP). Median MTC, coefficient of variation (CV), and tripping probability (TP) for obstacles of 10 and 15 mm were compared across ankle type in ascent and descent. FindingsWhen using AHA-MP, larger MTC median values for ascending (P ≤ 0.001, W = 0.58) and descending the ramp (P = 0.003, W = 0.47) were found in the prosthetic limb. Also significantly lower CV was found on the prosthetic limb for both types of AHA feet when compared to NAA for descending the ramp (P = 0.014, W = 0.45). AHA-MP showed the lowest TP for the prosthetic leg in three conditions evaluated. On the sound limb results showed the median MTC was significantly larger (P = 0.009, W = 0.43) and CV significantly lower (P = 0.005, W = 0.41) when using an AHA in ascent. InterpretationBoth AHA prosthetic designs help reduce the risk of tripping of the prosthetic limb by increasing the median MTC, lowering its variability and reducing TP for both legs when ascending and descending the ramp. For most of the conditions, AHA-MP showed the lowest TP values. Findings suggest that AHA prostheses, especially AHA-MP could reduce the risk of tripping on ramps in amputees.

Traffic generated by mixed-use developments—A follow-up 31-region study

This study aims to improve a previously-developed methodology for predicting the traffic impacts of mixed-use developments (MXDs). In 31 diverse metropolitan regions across the United States, we collected consistent regional household travel survey data and computed built environment characteristics—D variables—of MXDs. Multilevel modeling (MLM) was employed to predict the probability of trips captured internally within MXDs, walking on internal trips, and travel mode choice on external trips, by trip purpose. Larger, denser, mixed-use, and more walkable MXDs show a larger share of trips internally, compared with conventional suburban developments. Those MXDs with good access to transit, employment, and destinations also show higher levels of walking, biking, and transit shares on external trips, thus helping to reduce traffic impacts on the external road network. Perhaps the most impressive finding is that well-designed MXDs have walk shares of more than 50 percent on internal trips. A k-fold cross-validation supports the robustness of our analyses.

Development of state-based integrated dependability model of RPS in NPPs considering CCF and periodic testing effects at the early design phase

In the design process of safety critical digital systems, the quantitative analysis of their dependability is essential step to enhance the safety of nuclear power plants. Earlier this analysis can be performed, higher safety can be achieved in a more cost effective manner. In this paper, we present an integrated dependability evaluation model of the reactor protection system in nuclear power plants based on the Markov model. The complex interrelationship among dependability parameters such as architecture, channel-level failure, repair, common cause failure and periodic surveillance test are modeled systematically, which cannot be achieved by using conventional static methods. To prevent the integrated dependability model of reactor protection system from exploding the state, we treat each channel as the hyper component of which failure rate is the channel-level failure rate studied in the previous work. Using this concept, we simplify the model by incorporating various dependability parameters into the model. Using the model, quantitative results for reliability, availability, safety and spurious trip probability of reactor protection system are presented depending on various channel configurations. From the results, we can obtain insight into which parameters have the greatest effect on dependability and guideline for how to optimize parameters to satisfy the system requirements at the early design phase.