Investigation through New Approach on Plants with Antihypertensive Properties Used by the Herbalists in the Maritime Region of Togo

Like other countries in sub-Saharan Africa, hypertension is currently a public health problem in Togo. To decrease the insufficient of the methods previously used, a new survey technique, namely ATRM (Achat en Triplet des Recettes Médicinales), has been proposed. This study aims to contribute to a better knowledge of traditional remedies for their safe and sustainable use in the management of hypertension. ATRM method applied with 34 herbalists of 17 markets in maritime and Lomé-Commune health regions. Plant species and parts used, preparation and administration methods and market characteristics of plants were collected. In total, 62 plant species (56 genera and 30 families) were identified from 102 collected recipes. These recipes included 70% single plant recipes and 30% associated plant recipes, showing the influence of the ATRM method in reducing the number of plants in the recipes. Lippia multiflora Moldenke (23.50%) was the most used plant species followed by Uvaria chamae P. Beauv (8.20%), Acanthospermum hispidum DC. (4.92%), Lannea kerstingii A. Rich. (3.83%), Vitex doniana Sweet (3.83%), and Senna sieberiana DC. (3.28%). Leafy stems (33.33%), roots (20.83%) and trunk bark (16.67%) were the most used organs. The recipes were mainly prepared as a decoction and administered orally. In the transit market, Sorghum bicolor (L.) Moench (7.12 USD/kg), Senna occidentalis (L.) Link (4.98 USD/kg), Senna angustifolia Vahl (3.73 USD/kg) and Gardenia aqualla Stapf. & Hutch. (3.56 USD/kg) were the most expensive plants. 59.18% of the plant parts sold were roots, fruits, seeds, trunk bark and whole plants. These results suggest, on the one hand, an extensive biological investigation for effective management of hypertension. On the other hand, there is an urgent need to preserve the species whose vital organs were heavily sold.

Research on Fire Resistance of Stainless Steel Car Body Floor Structure in Urban Rail Vehicles

At present, the fire performance of vehicles is highly concerned in domestic and international rail transit market projects [1], where the fire performance of the vehicle’s underframe is a crucial factor that restricts the fire safety of the entire rail vehicle. How to improve the fire resistance of vehicle underframe structure has become the focus and difficulty of vehicle fire engineering [2]. This article introduces main international fireproof standards applicable for urban rail vehicles. Then, based on the typical underframe structures of urban rail vehicles stainless steel body, three refractory structure models on different underframe structures are established by using different fireproof insulation materials as underframe fillers. Moreover, fire resistance tests are conducted on samples of three underframe models to verify the effectiveness of those fire performance. As per the fire test results, the crucial indexes such as heat insulation, fire resistance, temperature rise curve, fire resistance time and material cost of different filler insulation materials are compared and summarized. Finally, the standardized and modular design specification is suggested on refractory underframe structures in urban rail vehicles.

Development of Fuzzy Level of Service Criteria for Bus Rapid Transit considering User Heterogeneities in China

Level of service (LOS) analysis based on LOS criteria is essential for the planning, design, and operational evaluation of public transit. However, there are no systematic transit LOS criteria at present in China. Bus rapid transit (BRT) is receiving increasing attention worldwide. Therefore, this study addresses LOS criteria for BRT in China. Transit passengers are heterogeneous in their perceptions, needs, and behavior. The traditional hard LOS criteria have an inherent weakness, because of which the accuracy of an LOS analysis is limited. Thus, in this study, we initially conducted transit market segmentation to reduce heterogeneity and subsequently developed BRT fuzzy LOS criteria for different passenger groups. Using a smartphone-based transit travel survey system, we organized BRT passenger travel surveys on three BRT systems in China to collect data. Transit market segmentation was performed based on user perceptions; passengers were segmented into a calm passenger group and an anxious passenger group using the latent class model. Passenger arrival time, passenger wait time, and running speed of the bus were selected as service metrics to reflect the BRT’s LOS. BRT fuzzy LOS criteria for the three service metrics in the case of both the calm and anxious passenger groups were developed using fuzzy C-means clustering. The LOS criteria for the two groups of passengers fit their psychological characteristics and reflected their personalized travel needs. Fuzzy LOS criteria can describe to what extent service metric values belong to the adjacent LOS categories via the use of membership. Thus, fuzzy LOS criteria can overcome the weakness of hard LOS criteria.

Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification

The study aims at utilizing a persona-based approach in understanding, and further quantifying, the preferences of the key transit market groups and estimating their willingness to pay (WTP) for service improvements. The study adopted an Error Component (EC) interaction choice model to investigate personas’ preferences in a bus service desired quality choice experiment. Seven personas were developed based on four primary characteristics: travel behaviour, employment status, geographical distribution, and Perceived Behavioural Control (PBC). The study utilized a dataset of 5238 participants elicited from the Hamilton Street Railway Public Engagement Survey, Ontario, Canada. The results show that all personas, albeit significantly different in magnitude, are negatively affected by longer journey times, higher trip fares, longer service headways, while positively affected by reducing the number of transfers per trip and real-time information provision. The WTP estimates show that, in general, potential users are more likely to have higher WTP values compared to current users except for at-stop real-time information provision. Also, there is no consensus within current users nor potential users on the WTP estimates for service improvements. Finally, shared and unique preferences for service attributes among personas were identified to help transit agencies tailor their marketing/improvement plans based on the targeted segments.

Forecasting bus ridership using a “Blended Approach”

As sources of “Big Data” continue to grow, transportation planners and researchers seek to utilize these new resources. Given the current dependency on traditional transportation data sources and conventional tools (e.g., spreadsheets and propriety models), how can these new resources be used? This research examines a “blended data” approach, using a web-based, open source platform to assist transit agencies to forecast bus ridership. The platform is capable of incorporating new Big Data sources and traditional data sources, using modern processing techniques and tools, particularly Application Programming Interfaces (APIs). This research demonstrates the use of APIs in a transit demand methodology that yields a robust model for bus ridership. The approach uses the Census Transportation Planning Products data, modified with American Community Survey data, to generate origin–destination tables for bus trips in a designated market area. Microsimulation models us a transit scheduling specification (General Transit Feed Specification) and an open source routing engine (OpenTripPlanner). Local farebox data validates the microsimulation models. Analyses of model output and farebox data for the Atlantic City transit market area, and a scenario analysis of service reduction in the Princeton/Trenton transit market area, illustrate the use a “blended approach” for bus ridership forecasting.

Using Smart Farecard Data to Support Transit Network Restructuring: Findings from Los Angeles

Recent technological innovations have changed why, when, where, and how people travel. This, along with other changes in the economy, has resulted in declining transit ridership in many U.S. metropolitan regions, including Los Angeles. It is important that transit agencies become data savvy to better align their services with customer demand in an effort to redesign a bus network that is more relevant and reflective of customer needs. This paper outlines a new data intelligence program within the Los Angeles County Metropolitan Transportation Authority (LA Metro) that will allow for data-driven decision-making in a nimble and flexible fashion. One resource available to LA Metro is their smart farecard data. The analysis of 4 months of data revealed that the top 5% of riders accounted for over 60% of daily trips. By building heuristics to identify transfers, and by tracking riders through space and time to systematically identify home and work locations, transit trip tables by time of day and purpose were extracted. The transit trip tables were juxtaposed against trip tables generated using disaggregate anonymized cell phone data to measure transit market shares and to evaluate transit competitiveness across several measures such as trip length, travel times relative to auto, trip purpose, and time of day. Relying on observed trips as opposed to simulated model results, this paper outlines the potential of using Big Data in transit planning. This research can be replicated by agencies across the U.S. as they reverse declining ridership while competing with data-savvy technology-driven competitors.

Public Transit Market Research of Low-Income Commuters Using Attitude-Based Market Segmentation Approach: Case Study of Fushun, China

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