Departure Time Research Articles

A chiral metal–organic polyhedron used as stationary phase for gas chromatographic separations

Metal-organic polyhedra (MOPs), as a novel class of porous materials, exhibit distinctive application potential in molecular recognition, separation, catalysis, luminescent materials, drug delivery, and biomedicine owing to their tunable geometric shape and size, well-defined cavities, and discrete characteristics. In this study, we report that a chiral MOP [Cu12(LV)12(H2O)12] (V = L-valine) coated capillary column was used for gas chromatographic separations. The MOP coated capillary column offered the efficient separation of n-alkanes, alkylbenzenes, n-alcohols, multiple types of isomers, and enantiomers with high selectivity. Various enantiomers including esters, halogenated hydrocarbons, alcohols, ethers, aldehydes, and amino acid derivatives were well resolved on this chiral column, especially for some amino acid derivatives (e.g., Rs = 5.24 for tyrosine derivative). Compared with the commercial β-DEX 120 column for separation of the tested racemates, the chiral resolution performance of the MOP coated column can be complementary to that of β-DEX 120 column. Using 1,2-dichloropropane and o, m, p-dichlorobenzene as analytes, the MOP coated column exhibited good stability and reproducibility for gas chromatographic separations. The relative standard deviations (RSDs) of the retention time and peak area for repeated separation of 1, 2-dichloropropane and o, m, p-dichlorobenzene were less than 1.15 and 0.79 %, respectively. The results show that the chiral MOP holds great potential as a novel type of CSP for application in gas chromatography.

Guidelines for bus speed at intersections under travel time control

To improve the traffic efficiency of bus at a group of intersections and reduce their delay, this paper develops a speed guidance strategy for buses under travel time control at intersection groups. The approach considers the influence of bus departure time, bus station arrival rate, and bus station dwell time. Simulation experiments are carried out on three consecutive intersections along Jinshan Avenue in Fuzhou, China. Results show that the average travel time of buses, the average delay of buses, and the average delay per capita in the priority direction are reduced by 5.5%, 9.9%, and 9.4%, respectively. The optimal rate of the average number of bus stops reached 15.2%. The indicators of the non-priority direction of social vehicles are not significantly affected. It shows that the efficiency of buses has improved after implementing the speed guidance strategy at the intersection groups without affecting the social vehicles as far as possible.

StreetSavvy: Automated Parking Management System with License Plate Recognition and Pricing

The Parking Charge Calculator represents a meticulously designed system aimed at accurately determining the parking charges for vehicles within a parking facility. This innovative solution leverages Automatic Number Plate Recognition (ANPR) technology in conjunction with a robust database system to streamline the process of identifying vehicles and computing corresponding parking fees. Within the confines of a parking lot, the unique identification of vehicles is facilitated through the extraction of information from license plates. When a vehicle enters the parking lot, the Parking Charge Calculator initiates the retrieval of the license plate number through the ANPR technology. This automated recognition system efficiently captures and records the alphanumeric sequence on the license plate, laying the foundation for subsequent charge calculations. The fundamental mechanism employed by the Parking Charge Calculator involves the multiplication of hourly parking charges by the temporal duration of a vehicle's stay. As the vehicle enters the parking lot, the system records the entry time. Upon the vehicle's departure, the departure time is similarly recorded. The time difference between these two instances becomes a critical parameter in the calculation process. Hourly parking charges, predetermined and configured within the system, serve as the unit of measurement for the calculation. The Parking Charge Calculator multiplies these charges by the elapsed time, providing a precise and automated method for determining the parking fee for a given vehicle. This approach not only ensures accuracy in charge computation but also eliminates the need for manual intervention, promoting efficiency and reducing potential errors. The integration of ANPR technology and a sophisticated database system not only expedites the identification process but also contributes to the overall reliability and security of the Parking Charge Calculator. By automating the parking charge calculation based on vehicle entry and exit times, this system offers a seamless and transparent method for both parking lot operators and vehicle owners, enhancing the overall parking experience and fostering a more technologically advanced approach to parking Management

Forecasting urban electric vehicle charging power demand based on travel trajectory simulation in the realistic urban street network

This paper presents a spatial-temporal urban charging power demand forecasting method based on EV travel trajectory simulation in the integrated urban street network and functional zones. Each EV trip is simulated via a travel trajectory based on the EV user’s travel behavior, which refers to the user’s decision-making processes regarding the choices of trip purpose, departure time, destination, trip route, parking time, and the time, location, and mode of EV charging. EV’s daily travel trajectory is built by merging the travel trajectories of the EV’s daily trips. The spatial-temporal slow and fast urban charging power demands are predicted using the simulated daily travel trajectories of available EVs in the urban area. The proposed simulation method is applied in the urban area of Campinas, Brazil. The results indicate that EVs’ daily travel trajectories are realistically produced to predict the daily charging load profiles at urban activity locations and functional zones.

Appraised Value of 3D Echocardiography Combined with the Triglyceride–Glucose Index to Evaluate the Long-Term Prognosis of Patients after Percutaneous Coronary Intervention

Objective: This study analyzed three-dimensional echocardiography (3DE) combined with the triglyceride–glucose (TYG) index to evaluate the long-term prognosis of patients after percutaneous coronary intervention (PCI). Methods: The clinical data of 102 patients who were treated with PCI after admission to our hospital from January 2020 to December 2020 were retrospectively analyzed. All the patients were followed up for 24 months to evaluate their long-term prognosis. The occurrence of cardiovascular and cerebrovascular events in all the patients was recorded. Cardiovascular and cerebrovascular events refer to a series of diseases or conditions of the heart and the cerebrovascular system, including sudden cardiac death. Patients with cardiovascular events were assigned to the exposed group, while those without cardiovascular events were included in the nonexposed group. The left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left ventricular mass index (LVMI), left ventricular remodeling index (LVRI), left ventricular ejection fraction (LVEF), standard deviation of time to peak of left ventricular 16 segments (Tmsv16-SD), maximum time difference (Tmsv16-Dif), and difference between the 3DE index and the TYG index were collected. Logistic regression analysis was performed on the indicators with differences to analyze the influencing factors of the long-term prognosis of patients after PCI. The receiver operating characteristic (ROC) curve was drawn. The sensitivity, specificity, area under the curve (AUC), and Youden index were calculated. The best predictive cutoff value was determined. The predictive value of the 3DE index and the TYG index, either alone or in combination, was observed for long-term prognosis after PCI. The relationship between the 3DE index and the TYG index was explored. Result: The 2-year follow-up results showed that 22 patients experienced cardiovascular events, and they were included in the exposed group, accounting for 21.57%. The remaining 80 patients without cardiovascular events were included in the nonexposed group, accounting for 78.43%. A significant difference was found in creatinine (Cr), high-density lipoprotein cholesterol (HDL-C), LVEDV, LVESV, LVMI, LVRI, LVEF, Tmsv16-SD, Tmsv16-Dif, and the TYG index between the exposed group and nonexposed group (p < 0.05). Cr, HDL-C, LVEDV, LVESV, LVMI, Tmsv16-SD, Tmsv16-Dif, and the TYG index in the exposed group were higher than those in the nonexposed group (p < 0.05). The exposed group also had lower LVRI and LVEF than the nonexposed group (p < 0.05). Logistic regression analysis of the indicators with differences showed that Cr, HDL-C, LVEDV, LVESV, LVMI, LVRI, LVEF, Tmsv16-SD, Tmsv16-Dif, and the TYG index were the major factors that affect the long-term prognosis of patients after PCI, with odds ratio values >1. Correlation analysis showed that the TYG index was positively correlated with LVEDV, LVESV, LVMI, Tmsv16-SD, Tmsv16-Dif, and the TYG index (r = 0.565, 0.678, 0.696, 0.702, 0.788, 0.804, p < 0.05). Moreover, it was negatively correlated with LVRI and LVEF (r = –0.580, –0.674, p < 0.05). The sensitivity and specificity of the 3DE index combined with the TYG index in predicting long-term prognosis after PCI were 0.818 and 0.950, respectively, which were significantly higher than those of the 3DE index or the TYG index alone. The Youden index was 0.768, the AUC value was 0.922, and the optimal threshold was 36.64. Conclusion: The 3DE index and the TYG index were influencing factors for the long-term prognosis of patients after PCI, and a correlation existed between the 3DE index and the TYG index. The 3DE index combined with the TYG index can improve the predictive efficiency of the long-term prognosis of patients after PCI.

Identifying alternative stops for first and last-mile urban travel planning

AbstractUrban travelers today are seeking increasingly more information to plan their optimal trip, based on additional factors other than scheduled departure times. Still, some route planning applications provide a simple approach with a few parameter settings (e.g. to minimize travel time between two specific places at a certain time) and without any multimodal solutions. Our approach provides travelers with a set of non-dominated nearby stops that presents a number of traveler preferences in an easily comprehensible and quickly calculable manner. We display first and last-mile stops that fall on a Pareto front based on multiple criteria such as travel time, number of transfers, and frequency of service. Our algorithm combines stop and route-based information to quickly present the traveler with numerous nearby quality options for their itinerary decision making. We expand this algorithm to include multimodal itineraries with the incorporation of free-floating scooters to investigate the change in stop and itinerary characteristics. We then analyze the results on the star-shaped public transportation network of Göttingen, Germany, to show what advantages stops on the Pareto front have as well as demonstrate the increased effect on frequency and service lines when incorporating a broadened multimodal approach.

Dual-tracer laser-induced fluorescence thermometry for understanding bubble growth during nucleate boiling on oriented surfaces

Nucleate boiling is perhaps one of the most efficient cooling methodologies due to its large heat flux with a relatively low superheat. Nucleate boiling often occurs on surfaces oriented at different angles; therefore, understanding the behavior of bubble growth on various surface orientations is of importance. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms on oriented surfaces, a major source of enhanced heat dissipation. This work aims to accurately measure three-dimensional (3D), space- and time-resolved, local liquid temperature distributions surrounding a growing bubble on oriented surfaces that quantify the heat transfer from the superheated liquid layer during bubble growth. The dual tracer laser-induced fluorescence thermometry technique combined with high-speed imaging captures transient 2D temperature distributions within a 0.3 ºC accuracy at a 30 μm resolution. The results show that the temperature close to the heated surface and bubble interface exhibits an acute transient behavior at the time of bubble departure, and the growing bubble works as a pump to remove heat from the surface with a temperature difference of up to 10 °C during its growth and departure. The experimental results are compared with data available in the literature to validate the accuracy of the technique. It was found that the heat transfer coefficient close to the bubble interface and heater is approximately 1.3 times higher than the heat transfer coefficient in the bulk liquid.

Stated opinions and potential travel with DRT – a survey covering three different age groups

ABSTRACT Previous research shows that well-grounded knowledge of the intended travellers is essential for achieving successful DRT services. However, there is a lack of understanding of the potential travels in different age groups, and the acceptance of design alternatives that may affect these. Based on surveys conducted in Sweden, this paper investigates these factors in a hypothetical DRT service, in the age groups 6–17, 18–69 and over 70 years. A total of 1241 people answered the questionnaires. The results show that the age groups have similar acceptance regarding delays and departure time intervals. Older adults have lower acceptance of digital solutions, and children and older adults have stronger requirements for value-added services. About 85–90% of the respondents claim they would use the service, at least occasionally. The results also show how a DRT system should be configured to allow room for system efficiency gains, while still being accepted by most travellers.

Influence of age on spatial and temporal migratory patterns of Black Storks from Germany

AbstractHow the migration behavior of long-lived birds develops throughout their life is still a research question in ornithology that is largely unanswered. Here, we analyzed GPS data of 70 juvenile Black Storks during the first 3 years of their migration to determine the influence of age and experience on migration patterns. We focused on the choice of the migration flyway, as well as the movements in the wintering area, and the changes in departure times with increasing age. Migratory routes of German Black Storks were divided into the western (82% of all tracked individuals) and eastern flyways (16%). The central migratory flyway through Italy or Greece was taken only by two individuals and never repeatedly. In addition, we found that the wintering area decreased by almost 60% during their first 3 years of life. In addition, spring departure dates from the wintering areas also became earlier with increasing age. However, we did not find changes in departures from the breeding area in autumn. Thus, our study shows that age and migration experiences have a considerable influence on essential migratory decisions in Black Storks. Further, it also demonstrates that older storks rely strongly on previous experiences to perform their migrations efficiently.

Crowd evacuation with human-level intelligence via neuro-symbolic approach

Understanding human response to crowd emergencies is extremely complex, and it plays a significant role in engineering construction designs and crowd safety. Individual choices, reasoning, and behaviours cannot be fully described by equations or rule-based methods. Accordingly, this research proposes a neuro-symbolic approach for modelling agents with human-level capabilities of reasoning and performance in an emergency evacuation. The proposed neuro-symbolic approach combines deep reinforcement learning with evaluative fuzzy logic to address the challenges of large amounts of required data, time, and trials-and-errors for policy optimization and to handle the assumption of reward function that may not be practical in real scenarios. This neuro-symbolic model has the potential to deal with the complexity of the environment and decision-making process via deep reinforcement learning and enhances the cognitive and visual intelligence via an evaluative fuzzy function, which continuously evaluates agent actions during the training process to boost pedestrian active response to their surroundings, with full awareness of time, thereby, the human-level capacity of reasoning. Moreover, this proposed model optimizes the computational demands of deep reinforcement learning and enables faster learning of new situations. The findings indicate that the proposed model can produce behavioural patterns that align with real observations of crowd evacuation, such as laminar flow, stop-and-go flow, and crowd turbulence. On top of that, a new evacuation behaviour is observed, as some pedestrians avoid congestion at the exit until the density reduces which reflects a level of human reasoning. The proposed model illustrates a higher accuracy and much faster converge than the pure proximal policy optimization model with substantially minimal training timesteps of as little percentage as 2 to 8. Meanwhile, the reliability study records an increase of the mean and standard deviation of evacuation time from 39.7 s, 1.06 to 155.09 s, 7.39 as crowd size increases from 15 to 200 pedestrians, which implies a rise of uncertainty. Therefore, we perceive that this work can provide crowd authorities and construction engineers with insights into complex behaviour and critical conditions to make better evacuation plans and sustainable designs to ensure crowd safety. It also provides a promising alternative to the evident lack of data on critical crowd conditions.

First train timetabling and passenger transfer routing problems in urban rail transit networks

This paper incorporates the shortest passenger travel paths into the first train timetabling problem in urban rail transit networks. We first propose a path-based first train timetabling problem, which is formulated as a mixed-integer nonlinear programming model to reduce the travel time for passengers. To solve this model, we present a tailored branch-and-bound combined with the timetable-based Dijkstra algorithm. To further enhance the efficiency of the exact algorithm, we design a rolling time heuristic algorithm as a substitute for the branch-and-bound algorithm. Then, we proved that the rolling time heuristic combined with the timetable-based Dijkstra algorithm achieves 97.86% and 99.39% reductions in CPU time while sacrificing only 0.007% and 0.057% of optimality gap by two numerical experiments. Finally, the results based on two test networks with different structures demonstrate that the path-based first train timetabling model effectively improves passenger travel efficiency. Specifically, the travel times are reduced by 744 mins and 761 mins, while the transfer waiting times are reduced by 37.87% and 58.91% compared to the existing timetables. Moreover, the results also demonstrate that loop line networks will increase the difficulty of timetabling and lead to many passengers taking detours. To further enhance the efficiency of commuting for passengers who take the first train, URT operators may consider relaxing the limitations on the departure time of the depot stations.

The Effect of Rocking Bed on Mood and Attention

Objective: The current study aims to explore the effects of sleep in the rocking bed on mood and attention.Methods: Thirty-nine young adults (14 with insomnia and 25 without insomnia) slept in the Sway Bed under three different conditions; 1) motionless condition, 2) continuously rocking condition, and 3) before-sleep rocking condition. In the motionless condition, participants slept without rocking for all night. In the continuously rocking condition, rocking started when lights out and stopped at wake-up time. In the before-sleep rocking condition, rocking started when lights out, but stopped after sleep onset. The Profile of Mood States and the computerized Comprehensive Attention Test were conducted before and after experimental sleep.Results: Under continuously rocking, depression, tension, anger, fatigue, confusion, and standard deviation of reaction time in divided attention were reduced. Under after-sleep bed condition, depression, tension, fatigue, confusion and omission errors in divided attention were reduced. There were no significant differences in after-sleep mood or attention changes between bed conditions. However, within those with insomnia, after-sleep improvement of depression and divided reduction were larger under continuously rocking conditions than under motionless conditions.Conclusion: Sleeping in the rocking bed improved negative affects and divided attention. The effects of rocking bed on depression and divided attention were prominent especially in those with insomnia. The current findings suggest that the sleep in the rocking bed may improve the depressive mood and the impaired attention of insomnia patients.

Evaluation of clinical outcomes of raised intraocular pressure following intravitreal triamcinolone acetonide injection.

Aim: To assess the incidence, risk factors, and treatment outcomes in intravitreal triamcinolone acetonide injection (IVTA) induced intraocular pressure rise and to compare IOP rise in 1-mg and 2-mg IVTA. Materials and methods: Prospective observational study conducted in all eyes receiving IVTA. Any pre-existing glaucoma and patients who received IVTA or dexamethasone implant in the last 6 months were excluded. Results: 9 between 61-70 years of age developed an IOP spike. The mean and standard deviation of age in years was 61.95 ± 8.70. Maximum eyes had ME due to Diabetic Retinopathy (53.3%). All cases of uveitic ME were reported to have an IOP spike. 2 out of 3 high myopic eyes and 1 eye with thyroid abnormality had an IOP spike. High IOP was found in 13 eyes, with more than 25 mm Hg rise in 4 eyes and more than 5 mm Hg rise from baseline IOP in 9 eyes. The mean and standard deviation of time taken for IOP raise (in days) was 46.39 ± 37.68. A total of 38 eyes received 1 mg of IVTA and the rest 22 received 2 mg of IVTA. 23.7% of 1 mg eyes experienced an IOP rise while it was 18.2% in eyes with 2 mg IVTA. The injection was repeated in 12 eyes and 41.7% developed an IOP spike among them. The independent "t" test results showed that there was a significant difference in the mean of IOP (Pre-injection) concerning the IOP rise (P=0.007*). 1 eye had IVTA crystals in the anterior chamber with raised IOP of 30 mm Hg. 1 out of 13 eyes with raised IOP needed 2 AGMs, the other 12 eyes responded well to 1 AGM. Discussion: IVTA is widely used in refractory cases of ME and steroid-induced glaucoma is the most common side effect of IVTA. To the best of our knowledge, there is a lack of literature on prospective studies on IVTA-associated risk factors, patterns of IOP elevation, and treatment outcomes. The pre-injection mean ± SD baseline IOP for uneventful eyes was 12.87±2.65 and the pre-injection mean IOP for eyes with IOP event was 15.23±2.89 (P=0.007*). Conclusion: We proposed that TA is an independent risk factor for post-intravitreal injection IOP spike. IVTA causes a maximum IOP spike at 1 to 2 months and has a protracted course that responds to anti-glaucoma medications. High baseline IOP, a repeated dose of IVTA, the presence of TA crystals in the anterior chamber, and high myopia were associated with significant IOP elevation. Abbreviations: ACD = Anterior chamber depth, AS = Anterior segment, AGM = Anti-glaucoma medications, ARMD = Age-related macular degeneration, BCVA = Best-corrected visual acuity, BRVO = Branch retinal vein occlusion, CCT = Central corneal thickness, CRVO = Central retinal vein occlusion, CME = Cystoid macular edema, CNVM = Choroidal neovascularization membrane, CSME = Clinically significant macular edema, DR = Diabetic retinopathy, ERM = Epiretinal membrane, IOP = Intraocular pressure, IGS = Irvine-Grass syndrome, GAGs = Glycosaminoglycans, IVTA = Intravitreal triamcinolone acetonide injection, ME = Macular edema, NVG = Neovascular glaucoma, OHT = Ocular hypertension, PDS = Pigment dispersion syndrome, PACG = Primary closed angle glaucoma, POAG = Primary open-angle glaucoma, PXF = Pseudoexfoliation, VA = Visual acuity, VEGF = Vascular endothelial growth factors, VH = Vonherick's grading, SD = Standard deviation, TA = Triamcinolone acetonide, TIGR = Trabecular meshwork inducible glucocorticoid response.

Investigation of Different Communication Topologies for Cooperative Adaptive Cruise Control Systems

After Adaptive Cruise Control (ACC) system applications, Cooperative Adaptive Cruise Control (CACC) systems are becoming an important part of automotive technology and industry in autonomous vehicles convoy applications. Together with this developing technology, CACC systems use vehicle to vehicle (V2V) communication to automatically transmit the movement information of vehicles. In this context, ACC systems use Radar or LIDAR measurements while CACC systems also consider the acceleration of the preceding vehicle. In this paper, the forms of information transmission between vehicles in autonomous vehicle convoys using CACC systems have been examined. From these forms of information transmission, the leader following, the predecessor follow-ing and the leader – predecessor following topologies have been considered. For each topology, an autonomous vehicle convoy consisting of eight vehicles was modeled in the MATLAB/Simulink environment. The feedforward and the feedback control system structure were given for CACC and ACC systems. For different communication topologies, the position-time, the speed-time, the acceleration-time and the headway time-time results were obtained. The maximum intervehicle distance error plots for each vehicle in different topology convoys were given to analyze the dynamic behavior of the convoys. The results have been analyzed in terms of the maximum intervehicle distance, the maximum speed, the minimum and the maximum acceleration, and the maximum headway time deviation from the desired headway time.

Travel behavior and system dynamics in a simple gamified automated multimodal network

Automated Vehicles (AVs) are poised to disrupt travel patterns and the sustainability of transportation networks. Conventional methods for studying these changes, such as stated preference surveys and agent-based simulations, have limitations. Serious games offer a promising alternative, providing a controlled and engaging environment for investigating travel behavior. In our study, 200 participants, grouped into sessions of 10, engaged in a competitive serious game simulating 50 daily choices of travel mode and departure time across three automated options. Two scenarios were examined: one with recurring congestion and another with nonrecurring congestion. Automated transit had fixed schedules, while private and shared rides could adapt to a congested bottleneck. Results revealed that ridesharing dominated, reaching 60% mode share under recurring congestion, displacing transit, and a comparative equilibrium emerged between shared and private rides. In the nonrecurring congestion scenario, ridesharing dropped to 37%, and a comparable multimodal equilibrium developed. Participants rarely achieved the optimal score, attaining a maximum of 88% of its potential. This study highlights a policy paradox: unregulated AV traffic can reduce transit use, exacerbate recurring congestion, yet necessitate increased transit investment to address nonrecurring congestion, confirming the Downs-Thomson paradox. Creating appealing mass transit alternatives is imperative to ensure efficiency and sustainability in the era of automated mobility.

SPHYNCS: Feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over one year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a compliance metric to evaluate the usability of the wearable device, we found an overall compliance rate of 80% over one year. We calculated daily physical activity, heart rate, and sleep parameters from two weeks of greatest compliance to compare NT1 (n=20) and HC (n=9) subjects. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p=0.007) and awakening index (p=0.025), as well as standard deviation of time in bed (p=0.044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p=0.019), and sleep latency (p=0.001), as well as a lower peak heart rate (p=0.008), heart rate standard deviation (p=0.039) and high-intensity activity (p=0.009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Quantum state preparation via engineered ancilla resetting

In this theoretical investigation, we examine the effectiveness of a protocol incorporating periodic quantum resetting for preparing ground states of frustration-free parent Hamiltonians. This protocol uses a steering Hamiltonian that enables local coupling between the system and ancillary degrees of freedom. At periodic intervals, the ancillary system is reset to its initial state. For infinitesimally short reset times, the dynamics can be approximated by a Lindbladian whose steady state is the target state. For finite reset times, however, the spin chain and the ancilla become entangled between reset operations. To evaluate the protocol, we employ Matrix Product State simulations and quantum trajectory techniques, focusing on the preparation of the spin-1 Affleck-Kennedy-Lieb-Tasaki state. Our analysis considers convergence time, fidelity, and energy evolution under different reset intervals. Our numerical results show that ancilla system entanglement is essential for faster convergence. In particular, there exists an optimal reset time at which the protocol performs best. Using a simple approximation, we provide insights into how to optimally choose the mapping operators applied to the system during the reset procedure. Furthermore, the protocol shows remarkable resilience to small deviations in reset time and dephasing noise. Our study suggests that stroboscopic maps using quantum resetting may offer advantages over alternative methods, such as quantum reservoir engineering and quantum state steering protocols, which rely on Markovian dynamics.

What Lies Behind Fare Evasion in Curb Parking Behavior? Evidence from Nanjing, China

Frequent incidents of parking fare evasion pose ongoing challenges, placing parking management departments under pressure as a result of reduced parking revenues. The causal mechanisms behind parking fare evasion, as identified in existing studies, remain incompletely explained. To address this gap, our study utilized empirical data from Nanjing, China, employing a random forest methodology to elucidate the intricate and non-linear relationships among parking site attributes, weather conditions, and the built environment concerning curb parking fare evasion behavior. Additionally, we used the Shapley Additive Explanations method to clarify the pertinent variables. Our empirical findings emphasize the significant impact of parking site attributes, including departure time, parking space availability, and occupancy, on evasion behavior. Notably, our results underscore the necessity of strategically managing parking near scenic spots and points of interest (POIs) associated with accommodation during weekends. Conversely, on workdays, there is a need for strategic oversight of parking fees in the vicinity of government institutions, scenic spots, and workplace-related POIs. These research outcomes make valuable contributions to the existing literature on curb parking and provide actionable insights for improving urban parking management.

Optimization of ticket pricing for high-speed railway considering full competitive environment

The rapid development of high-speed railways (HSR) worldwide has provided a fast, convenient, safe, and comfortable mode of transportation. It is also an important public transportation tool for green travel, contributing to the reduction of carbon emissions. HSR has a strong competitive advantage in long-distance passenger transportation, but it faces significant competition from the aviation industry. Airlines attract passengers with discounted fares and differentiated pricing strategies, apart from shorter travel times. Therefore, it is necessary for railway enterprises to enhance their competitiveness through differentiated pricing methods for HSR. In this study, we focus on optimizing a long-distance origin-destination (OD) pair and creating a competitive market environment by considering all operating trains and flights for the selected OD pair. We aim to improve the realism of the study by considering passengers’ fully rational travel choice behavior. To reduce the complexity of variables, passengers are grouped based on their expected departure time and income level. A mixed integer linear programing model is developed to address the problem, aiming to minimize overall travel costs for passengers while ensuring that the income of the railway enterprise does not fall below a specified value. By setting this specific value, the maximum revenue of HSR train tickets can be obtained.

A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms

Forecasting tram passenger flow is an important part of the intelligent transportation system since it helps with resource allocation, network design, and frequency setting. Due to varying destinations and departure times, it is difficult to notice large fluctuations, non-linearity, and periodicity of tram passenger flows. In this paper, the first-order difference technique is used to eliminate seasonal structure from the time series data and the performance of different machine learning algorithms on passenger flow forecasting in trams is evaluated. Furthermore, the impact of the Covid-19 pandemic on forecasting success is examined. For this purpose, the tram data of Kayseri Transportation Inc. for the years 2018-2021 are used. Different estimation models including Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, Convolutional Neural Network, and LongTerm Short Memory are applied and the time series forecasting performances of the models are evaluated with MAPE and R2 metrics.