Average Trip Research Articles

SCALABILITY IMPROVEMENT OF ACTIVE PROBING FOR FAILURE DETECTION IN LARGE-SCALE SOFTWARE DEFINED NETWORKS USING COMMUNITY DETECTION ALGORITHMS

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

Telehealth experience during COVID-19 at an American burn Association (ABA) verified adult and pediatric burn center

IntroductionThe COVID-19 pandemic resulted in unprecedented changes to healthcare services. Non-emergent, out-patient care was either discontinued, greatly reduced, or switched to telehealth during the first wave of the pandemic. Here, we describe an American Burn Association (ABA) verified Burn Center’s experience with telehealth services during and after the COVID-19 emergency. Material and methodsIn this retrospective study, all patients who underwent out-patient care at a large academic hospital’s ABA Burn Center between March 2018 and March 2023 were identified from the electronic medical record system. Descriptive analysis was carried out to delineate trends in outpatient in-person and telehealth services during the COVID-19 pandemic. The travel distance saved from the introduction of telehealth was determined. ResultsDuring the study period, 3471 patients underwent a total of 7444 out-patient visits for burn care. There were no telehealth visits prior to the onset of the COVID-19 pandemic. In the first year of the COVID-19 pandemic, 14.9% of all out-patient visits were conducted with telehealth. This decreased to 8.3% and 6.8% of all out-patient care in the second and third years of the pandemic, respectively. The average round trip travel distance saved was 123 miles (2.8–––2312 miles). No complications were reported specific to receiving telehealth care. ConclusionsTelehealth is a feasible option for out-patient burn care in selected patients and reduces travel for patients. Further studies are needed to assess patient and clinician satisfaction, clinical outcomes, and the economic impact of telehealth utilization to help guide appropriateness of use.

Motorcycle Police Training and Service Vehicle Development in Hungary


 The history of the police motorcycle service is almost 100 years old, dating back to the Horthy era in the mid-1920s in Hungary. A turbulent history has interrupted his development several times, but he has remained continuous. Staffing levels peaked in the 1990s, and technology has developed rapidly, focused, and strongly since 2017. As a result, effective implementation of the service's mission system will depend on training development that can move at a pace with technical development and deliver systemic results in implementing missions. In this article, the author examined the use of police personnel authorised to drive specialised motorcycles, their main functions and the determination of the seasonal mileage. A questionnaire survey of motorcycle police officers enrolled in the Basic Motorcycle Training and License Renewal Training Programme of the Police Education and Training Centre in 2022. According to aggregated data, the average annual trip of the country's total of 218 service vehicles is 3,599 km. The main patrol category 104 (officer, patrol leader, patrol commander, team commander) is significantly better with 6589 km/year, while the secondary patrol category 114 (event scene and investigation, district officer, patrol commander, driver category) is 2266 km on average in 2022. The results of the survey highlight the low average driving of motorcycle police. This is mainly because more than 50 per cent of trained officers do not have the major task of carrying out their duties on a service motorcycle. However, due to the high quality of their execution, the training system must be restructured to make it more effective.

Regional route guidance with realistic compliance patterns: Application of deep reinforcement learning and MPC

Solving link-based route guidance problems for large-scale networks is computationally challenging and faces practical issues, such as spatial–temporal data coverage. Thus, regional route guidance has emerged as a promising strategy, which utilizes regional approaches (i.e., network-level macroscopic traffic models) to capture traffic dynamics. Existing regional route guidance models have mainly focused on macroscopic flows and aggregated splitting rates, employing uniformly sampled vehicles as controllable targets. These models, however, overlook the inherent nature of individual drivers’ compliance. It may deteriorate the guidance performance as existing route guidance models cannot effectively generate customized route plans for compliant vehicles. This paper aims to introduce a regional route guidance framework with the utilization of different solution approaches, i.e. model-based and data-driven, considering the compliance pattern. In particular, MPC-based and deep reinforcement learning-based schemes are proposed for the information service provider to send customized route plans to compliant individuals. Within each solution approach, different route guidance strategies are developed for specific purposes and evaluated through numerical experiments under low- and high-congestion scenarios. Besides, the trade-off between total travel time and average trip length is studied, wherein the balance reward in a deep reinforcement learning-based approach enables controllable agents to reduce trip costs while compromising partial system utility. The findings indicate the effectiveness of the proposed strategies in alleviating network congestion. Additionally, multi-agent approaches outperform single-agent ones, highlighting the benefits of cooperative decision-making in traffic management.

A comparative study of bike-sharing and e-scooter sharing users and services in a college town during COVID-19

Shared micro-mobility services have seen a sharp increase during the last decade only to be hindered by the pandemic. Bike-sharing and e-scooter sharing services are the two main services of shared micro-mobility and are often perceived to be similar. However, they may differ because of different mobility trends, associated with each service, stemming from distinct usage habits, particularly in a campus setting, distinct from major cities. In this regard, there is limited understanding of how bike-sharing and e-scooter sharing services differ or are similar in terms of user characteristics within a campus context. Moreover, the effect of the COVID-19 pandemic on shared micro-mobility in campus settings has not been investigated. This study aims to: (a) uncover the users’ perceived barriers and benefits that are associated with each service, (b) determine whether user characteristics and preferences differ between bike-sharing and e-scooter users, (c) evaluate how travel habits and user characteristics can help determine trip purpose of shared micro-mobility services, and (d) detect any change in trip characteristics, namely trip purpose and length, due to COVID-19 for both services. To this end, a survey was conducted between October 2019 and August 2020 in West Lafayette, Indiana, home of Purdue University, gathering 615 responses. Comparative tests were carried out to evaluate the change in trip purpose and length of bike-sharing and e-scooter sharing trips during the pandemic. It was found that commuting trips increased in frequency after COVID-19, while the average trip length during COVID-19 increased for bike-sharing but not for e-scooters. Binary logistic regression results indicate that e-scooter sharing and bike-sharing users hold different characteristics and preferences, which suggests diverse usage patterns. The study findings offer valuable implications for transportation and policy planning in college towns.

Development of linear power operational amplifier power supply with pre-regulated voltage controller for TPS correction magnets

The Taiwan Photon Source (TPS) is a renowned 3 GeV synchrotron accelerator light source. Over the past decade of successful operation, it has demonstrated outstanding performance, notably in average beam trip downtime, meeting international standards. A continuous regimen of scheduled equipment upgrades has been maintained across all subsystems to ensure ongoing improvement and the preservation of an optimal research facility environment. The central focus of this study revolves around the design and realization of a TPS correction magnet power supply employing Linear Power Operational Amplifiers (LPOA) in conjunction with a pre-regulated voltage controller. The power source predominantly features APEX Corporation's PA05 LPOA, characterized by bipolar output, high internal power dissipation, and wide bandwidth. Additionally, a DC Current Transducer (DCCT) was employed as the current feedback element, integrated with the pre-regulator voltage controller to form a closed-loop current modulation circuit, providing the variable voltage required by the LPOA. Through these measures, we have successfully developed an LPOA power supply unit equipped with a pre-regulated voltage controller for TPS correction magnets. The prototype device boasts a maximum output current of ±10 A, operating at 24 V. Validation through control loop design has led to rapid and stable output current performance. The output current ripple is maintained within 100 μA, with a rise time of 75 μs during step response. During frequency response testing utilizing a 0.1 V disturbance signal, the gain margin resides within -3 dB at an 11.2 kHz bandwidth and the phase margin within -45∘ at a 5.1 kHz range. Long-term output current stability is sustained within ten ppm. Finally, a hardware prototype circuit has been constructed in the power supply laboratory, featuring a ±24 V input voltage and ±10 A output current, achieving a maximum rated power of 240 W.

Longitudinal changes in driving behaviours among older adults with different A/T/N biomarker profiles

AbstractBackgroundThe pathophysiological process of Alzheimer’s disease (AD) spans approximately 10‐15 years. To define AD by its pathologic processes, the A/T/N classification framework was proposed constituting ß‐amyloid (A), tau (T), and neurodegeneration (N) biomarkers derived from cerebrospinal fluid (CSF) or imaging. With these biomarkers, individuals can be divided into four categories: normal (A‐T‐N‐), AD pathologic change (A+T‐N‐), AD (A+T+N‐, A+T‐N+, A+T+N+), and suspected non‐AD pathology (SNAP) (A‐T+N‐, A‐T‐N+, A‐T+N+). Recent evidence indicates that daily driving behaviours can identify preclinical AD, solely based on CSF ß‐amyloid (A), with high accuracy. This study aims to determine the extent to which longitudinal changes in driving behaviour vary between individuals with different A/T/N profile categories.MethodParticipants were enrolled in a longitudinal study on driving and preclinical AD biomarkers from Washington University School of Medicine. Temporospatial driving behaviours were collected with a data logger installed in participant vehicles over a period between January 2015 and March 2020. CSF collected within three years of each driving event was used to assign A/T/N categories. The cohort included 132 cognitively normal (Clinical Dementia Rating of 0) older adults (>65). Changes in driving behaviours over time were modelled for each group using linear mixed‐effects models. A Z‐test was used to determine if the slopes significantly differed between individuals with normal biomarkers and the remaining groups.ResultParticipants were classified as 59 individuals with normal biomarkers, 9 with AD pathologic change biomarkers, 45 with AD biomarkers, and 19 with SNAP biomarkers. Compared to normal aging, the AD pathology group had a statistically significantly higher increase in average occurrences of under‐speeding (p = 0.001), a significant decrease in average trip duration (p = 0.021), and a significant decrease in the average standard deviation of vehicle speed (p = 0.029). The AD group had no significant differences from the normally aging group. The SNAP group showed a significantly higher increase in the average occurrences of over‐speeding (p<0.001) than normal aging.ConclusionDriving behaviours and their rate of change over time vary between individuals with different A/T/N profile categories. These differences provide an opportunity to strengthen our understanding of the disease trajectory.

Longitudinal changes in driving behaviours among older adults with different A/T/N biomarker profiles

AbstractBackgroundThe pathophysiological process of Alzheimer’s disease (AD) spans approximately 10‐15 years. To define AD by its pathologic processes, the A/T/N classification framework was proposed constituting β‐amyloid (A), tau (T), and neurodegeneration (N) biomarkers derived from cerebrospinal fluid (CSF) or imaging. With these biomarkers, individuals can be divided into four categories: normal (A‐T‐N‐), AD pathologic change (A+T‐N‐), AD (A+T+N‐, A+T‐N+, A+T+N+), and suspected non‐AD pathology (SNAP) (A‐T+N‐, A‐T‐N+, A‐T+N+). Recent evidence indicates that daily driving behaviours can identify preclinical AD, solely based on CSF β‐amyloid (A), with high accuracy. This study aims to determine the extent to which longitudinal changes in driving behaviour vary between individuals with different A/T/N profile categories.MethodParticipants were enrolled in a longitudinal study on driving and preclinical AD biomarkers from Washington University School of Medicine. Temporospatial driving behaviours were collected with a data logger installed in participant vehicles over a period between January 2015 and March 2020. CSF collected within three years of each driving event was used to assign A/T/N categories. The cohort included 132 cognitively normal (Clinical Dementia Rating of 0) older adults (>65). Changes in driving behaviours over time were modelled for each group using linear mixed‐effects models. A Z‐test was used to determine if the slopes significantly differed between individuals with normal biomarkers and the remaining groups.ResultParticipants were classified as 59 individuals with normal biomarkers, 9 with AD pathologic change biomarkers, 45 with AD biomarkers, and 19 with SNAP biomarkers. Compared to normal aging, the AD pathology group had a statistically significantly higher increase in average occurrences of under‐speeding (p = 0.001), a significant decrease in average trip duration (p = 0.021), and a significant decrease in the average standard deviation of vehicle speed (p = 0.029). The AD group had no significant differences from the normally aging group. The SNAP group showed a significantly higher increase in the average occurrences of over‐speeding (p<0.001) than normal aging.ConclusionDriving behaviours and their rate of change over time vary between individuals with different A/T/N profile categories. These differences provide an opportunity to strengthen our understanding of the disease trajectory.

Comparison of travel pattern before and during COVID-19 restrictions (ease and strict restrictions) in Greater Kuala Lumpur, Malaysia

Previous studies have investigated the effect of COVID-19 on travel behaviour, particularly on changing trends of daily trips and weekly trips for various trip purposes (work, shopping, recreational and social). Very few studies have assessed the effect of various COVID-19 restrictions (i.e., Conditional Movement Control Order (CMCO) and Movement Control Order (MCO)) on the trip duration.This study aims to 1) examine the changes in travel patterns, the frequency of daily and weekly trips and trip duration; 2) evaluate the significant difference between trip duration before COVID-19, during CMCO and MCO; 3) analyse the effect of COVID-19 restrictions on the trip duration of different age groups; and 4) investigate the influence of household characteristics, daily, and weekly trips on trip duration in Greater Kuala Lumpur Malaysia.The research was based on the data collected in Greater Kuala Lumpur, Malaysia, during CMCO (December 2020) and MCO (January 2021). The visualisation techniques (Chord and Sankey diagrams), statistical tests (McNemar and Spearman correlation), and multinomial logistic regression modelling were applied in this study.The results revealed that daily trips were directly linked with car availability during CMCO, weekly trips were indirectly associated with age, and medium (3 to 8 h) duration trips were directly linked with car availability and household size during MCO. Moreover, the work, recreational and other trips of 1 to 2 h were decreased, while grocery and dine-out trips of 1 to 2 h were increased during CMCO. COVID-19 restrictions affected the trip duration of older people, asolder people had shorter trips before COVID-19 and during CMCO. On the other hand,no association was found between age and average trip duration during MCO, which meansyounger and older people had the same proportion of shorter (0 to 2 h) duration trips during MCO. The Malaysian Government can utilise the results to set the pandemic-related transport policy.

Application of adaptive neuro-fuzzy inference system in modelling home-based trip generation

This study aims to investigate the feasibility of using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) for modelling home-based trip generation in Salfit City, Palestine. The research compares the performance of these two methods and provides insights into their efficiency for different trip purposes. The methodology involves developing separate trip generation models for various purposes, including total daily household trips generated (HBALL), home-based work (HBW), home-based education (HBE), and other home-based trips (HBO). The underlying principles and implementation details of the ANFIS architecture are also discussed. Objective evaluation metrics like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared are used to assess the effectiveness of each model. The results indicate that ANFIS performs well for modeling HBALL and HBO trips, which exhibit more complex behavior with wider data ranges and higher average daily trip counts. Compared to the MLR approach, ANFIS shows improved accuracy and closer predictions. For instance, when modelling HBALL trips, ANFIS achieved an RMSE of 1.4880, while MLR resulted in 1.7112, representing a reduction of 13.04%. However, for HBW and HBE trip purposes, where behavior is less complicated, MLR appears to be sufficient. The R-squared values obtained with MLR are sufficiently high to capture most of the trip variations, and the results between the two approaches are closely comparable. The R-squared value for MLR in HBE trips was almost identical to that of ANFIS, which was 96.65%. Similarly, for HBW trips, MLR achieved an R-squared value of 90.36%, while ANFIS performed slightly better with an R-squared value of 92.74%. In conclusion, ANFIS shows promise for modelling systems with complex behavior, while MLR remains a suitable option for less complicated scenarios. The study emphasizes the importance of exploring different modelling techniques in transportation research to identify the most appropriate approach for specific cases.

An efficient composite membrane to improve the performance of PEM reversible fuel cells

In this study innovative composite Nafion/GO membranes are tested at different GO loading (0.5% wt., 1% wt. and 1.5% wt.) in electrolyser and fuel cell mode (Unitized reversible fuel cell). Baseline Nafion membranes were used for comparison.Water uptake (WU), ion exchange capacity (IEC), tensile strength, TGA (thermogravimetric analysis) and SEM (scanning electron microscope) analysis are discussed. The SEM revealed the inclusion of GO into the Nafion matrix while the TGA showed an increased thermal stability of the membrane attributed to the inclusion of the carbon material. Moreover, the addition of GO improves the membrane tensile strength, obtaining a maximum enhancement of nearly 90%, while reducing the elongation ratio (80% for the Nafion and 45% for the 1.5% wt. GO membrane). Water uptake increased when increasing the content of GO due to its hydrophilic nature recording the highest values for the membrane with 1.5%wt. of GO (23% against 10% for the Nafion). An increase of IEC (almost 14%) is noticed when GO content is increased. The beneficial effect of GO on the IEC can be attributed to a non-uniform distribution of GO into the Nafion matrix but needs further investigation.Both fuel cell and electrolyser polarization curves were carried out using MEAs with an active area of 9 cm2 and a thickness of 50 μm. The temperature and the pressure were set to 20 °C and 1 atm respectively. Regarding fuel cell mode, the optimum loading of GO has been found to be 0.5%, registering the highest performance, 13% higher than Nafion. Regarding the electrolyser mode, the GO 0.5% wt. membrane, showed performance comparable to the Nafion. A comparison between Nafion based membranes at higher thicknesses showed that, adopting GO, it is possible to obtained similar performance with a reduced membrane thickness, keeping almost equal the performance and the average round trip efficiency (26.1% for the GO and 26.6% for the Nafion). In commercial applications such characteristics allows to strongly reduce the cost of materials.Durability and stability of the GO/Nafion membrane should be properly investigated in successive studies as such membranes are subjected to a rapid deterioration of their performance.

Optimal vehicle capacity and dispatching policy considering crowding in public bus transit services

This study investigates the often overlooked impact of on-board crowding on operational and user costs in public transit systems, specifically within a many-to-many bus transit line with varying demand patterns. While previous research has used mathematical programming for similar problems, this paper employs analytical approaches to offer deeper insights and address fundamental questions. First, we propose an approach to determine optimal bus capacities, factoring in in-vehicle crowding costs, assuming a fixed headway. Second, we explore the optimal dispatching policy for buses with fixed capacities, considering crowding costs. Third, we optimize both headway and vehicle capacity simultaneously. Our findings reveal that optimal vehicle capacity correlates with average passenger trip length and the square root of crowding-discomfort costs, especially when crowding increases linearly with load factor. When both headway and capacity are variable, smaller vehicles with shorter headways are favored, particularly in moderate-demand scenarios, especially with cost-effective staff models like autonomous fleets.

How do dockless bikesharing services affect public transit and taxi use? Evidence from 36 central cities in China

Despite the rapid growth in the popularity of technology-enabled dockless bikesharing services, comprehensive research on their causal effects on other modes of transportation is still scarce. To fill this gap, this study exploits the staggered entry of Ofo and Mobike into 36 Chinese central cities as a quasi-natural experiment using a difference-in-differences approach to examine the overall impact of dockless bikesharing services on bus, rail, and taxi use. The results show that the entry of dockless bikesharing services significantly reduced city bus ridership by 5.1% and taxi passenger miles by 6.4%, but had no significant impact on rail ridership overall. Further heterogeneity analysis shows that a higher proportion of BRT vehicles can attenuate the negative impact of bikesharing services on bus ridership, while bikesharing seems to promote rail transit use in cities with low population densities and limited rail transit networks. Moreover, the reduction effect of bikesharing services on taxi passenger miles was driven by a decrease in average trip distance, with the unintended consequence of total taxi deadheading increased by 9.7%. This study provides important and valuable references for city governments to better utilize bikesharing services to develop efficient and sustainable urban transportation systems.

Socio-Economic Potential Of Fishermen In Pulau Banyak, Aceh Singkil

Communities in Pulau Banyak District on average work in the fisheries sector as fishers. Pulau Banyak fishers take advantage of the potential of captured fisheries as their main source of income. The living conditions of fishers are often classified as a marginal group close to poverty. This study aims to examine the socio-economic conditions of fishers in the Pulau Banyak District of Aceh Singkil. The method used is to conduct a field survey using a questionnaire (interview). The location of the research was in Pulau Banyak District, Aceh Singkil Regency. The results of the study show that the social conditions of the Pulau Banyak people work as fishers with a low level of education, at most equivalent to elementary school. The economic condition of the fishers on Pulau Banyak is seen based on their main income and also other income sourced from side income and the income of family members who help fishers. The main source of income for fishers is an average of IDR 1,000,000-1,500,000 per month with an average trip of 6 times a week or 24 times a month with catches of 8 types of fish. While the pattern of fish marketing, the average fishers as much as 64 percent choose to sell to bench shops. The government has an important role in monitoring the marketing pattern of fishery products and implementing policies for optimal and sustainable utilization of fishery resources.

Traffic Flow Pattern Based Approach to Predict Real Driving Emission Test Routes

Due to the poor representation of NEDC emission test, the European Commission put into the force a new procedure, which contains both a laboratory test and a real word driving test. In this new test procedure, even though WLTC replaces NEDC, it is required to test the vehicle under real traffic conditions, which contains urban, rural and highway segments. Due to the new emission regulation, considerable number of academic work focus on the creation of RDE (Real Driving Emission) routes and RDE tests. In this study, it is introduced a new methodology for creating potential RDE routes. The routes, created in MATLAB code by using Istanbul road data containing a half an hour average trip records were used to predict a probable RDE test road. This model creates a number of RDE routes starting from defined coordinates and then analyses all alternative routes with respect to traffic flow rate and RDE boundary conditions identified by the European Commission. The routes obtained using the methodology developed are tested in real life conditions and evaluated according to actual existing traffic conditions. The agreement between the results of the method and the results of the actual driving test is limited in urban road section. However, for rural and highway road sections, there is a significant agreement between the predictions and actual test results.

An Evaluation of Smartphone Tracking for Travel Behavior Studies

The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Because of the limited burden, the risk of drop-out from the survey is significantly lower, making the actual data collection more efficient, even for longer survey periods. Finally, in the data analysis phase, the travel behavior indicators derived from smartphone tracking data result in higher average trip rates, shorter average trip lengths and a higher share of active modes (bike, walking) than the results from the paper survey. Although this is explained by more complete and more consistent trip registration, this finding is problematic for comparability between surveys based on different methods, both for longitudinal monitoring (comparability over consequent surveys) and for benchmarking (comparability over geographical areas). Therefore, it is crucial to clearly report the applied data collection methods when describing or comparing travel indicators. In surveys, a combined approach of both written trip diaries and smartphone tracking is advised, where each method can complement the shortcomings of the other.

Diagnosis of visceral leishmaniasis in Ceará state, Brazil: A flow analysis of cases between 2007 and 2021.

To analyse the flow of cases of visceral leishmaniasis (VL) in the state of Ceará, Brazil, between 2007 and 2021. An ecological study was conducted using a spatial approach of newly confirmed cases of VL recorded in the Notifiable Diseases Information System. We identified individuals whose municipality of diagnosis differed from that of their residence. Flow maps, constructed using Tabwin 32 and ArcMap 9.2, allowed for the identification of the volume of traffic between the municipality of residence and that of initial care. There were 6775 confirmed VL cases. As a flow indicator, 178 counties had at least one resident diagnosed in another municipality in Ceará, with 2491 VL cases and an average trip of 79 km. The largest hub for receiving cases for diagnosis was the capital Fortaleza (1478 patients from 129 other municipalities), followed by Sobral, located in the northwestern region of Ceará (599 from 55 municipalities), and Barbalha, in the southern region (171 from 29 municipalities). In this southern region, 25 municipalities moved 55 people for treatment to Juazeiro do Norte and 11 municipalities moved 39 patients to Crato. A total of 255 patients with VL from 11 municipalities in other Brazilian states, mainly from the Northeast and North, were observed and notified in health services in Ceará. The major centres of VL diagnosis outside residence were in the cities of Fortaleza, Sobral, Barbalha, Juazeiro do Norte and Crato. There was also an outflow of cases from other municipalities located in the northeastern and northern regions of Brazil. The flows were more intense during the first triennium of the analysis and milder from 2019 to 2021. Understanding the diagnostic flow of VL helps in decision making and the development of public policies to improve the lives of the population.

An Effective YOLO-Based Proactive Blind Spot Warning System for Motorcycles

Interest in advanced driver assistance systems (ADAS) is booming in recent years. One of the most effervescent ADAS features is blind spot detection (BSD), which uses radar sensors or cameras to detect vehicles in the blind spot area and alerts the driver to avoid a collision when changing lanes. However, this kind of BSD system fails to notify nearby vehicle drivers in this blind spot of the possible collision. The goal of this research is to design a proactive bus blind spot warning (PBSW) system that will immediately notify motorcyclists when they enter the blind spot or the area of the inner wheel difference of a target vehicle, i.e., a bus. This will increase the real-time functionality of BSD and can have a significant impact on enhancing motorcyclist safety. The proposed hardware is placed on the motorcycle and consists of a Raspberry Pi 3B+ and a dual-lens stereo camera. We use dual-lens cameras to capture and create stereoscopic images then transmit the images from the Raspberry Pi 3B+ to an Android phone via Wi-Fi and to a cloud server using a cellular network. At the cloud server, we use the YOLOv4 image recognition model to identify the position of the rear-view mirror of the bus and use the lens imaging principle to estimate the distance between the bus and the motorcyclist. Finally, the cloud server returns the estimated distance to the PBSW app on the Android phone. According to the received distance value, the app will display the visible area/blind spot, the area of the inner wheel difference of the bus, the position of the motorcyclist, and the estimated distance between the motorcycle and the bus. Hence, as soon as the motorcyclist enters the blind spot of the bus or the area of the inner wheel difference, the app will alert the motorcyclist immediately to enhance their real-time safety. We have evaluated this PBSW system implemented in real life. The results show that the average position accuracy of the rear-view mirror is 92.82%, the error rate of the estimated distance between the rear-view mirror and the dual-lens camera is lower than 0.2%, and the average round trip delay between the Android phone and the cloud server is about 0.5 s. To the best of our knowledge, this proposed system is one of few PBSW systems which can be applied in the real world to protect motorcyclists from the danger of entering the blind spot and the area of the inner wheel difference of the target vehicle in real time.

A general equilibrium model for multi-passenger ridesharing systems with stable matching

This paper proposes a general equilibrium model for multi-passenger ridesharing systems, in which interactions between ridesharing drivers, passengers, platforms, and transportation networks are endogenously captured. Stable matching is modeled as an equilibrium problem in which no ridesharing driver or passenger can reduce his/her ridesharing disutility by unilaterally switching to another matching sequence.This paper is one of the first studies that explicitly integrates the ridesharing platform’s multi-passenger matching problem into the model. By integrating matching sequence with hyper-network, ridesharing-passenger transfers are avoided in a multi-passenger ridesharing system. Moreover, the matching stability between the ridesharing drivers and passengers is extended to address the multi-OD multi-passenger case in terms of matching sequence. The paper provides a proof for the existence of the proposed general equilibrium.A sequence-bush algorithm is developed for solving the multi-passenger ridesharing equilibrium problem. This algorithm is capable to handle complex ridesharing constraints implicitly. Results illustrate that the proposed sequence-bush algorithm outperforms general-purpose solver, and provides insights into the equilibrium of the joint stable matching and route choice problem. Numerical experiments indicate that ridesharing trips are typically longer than average trip lengths. Sensitivity analysis suggests that a properly designed ridesharing unit price is necessary to achieve network benefits, and travelers with relatively lower values of time are more likely to participate in ridesharing.

Uncovering the spatio-temporal impact of the COVID-19 pandemic on shared e-scooter usage: A spatial panel model

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changesin e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.