柯桥区公交老年人早晚高峰数据
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用来评估柯桥区老年人出行需求和交通政策的影响。首先，通过了解早高峰老年人坐车的比例，可以评估老年人的出行需求和偏好。例如，如果老年人在早高峰期间乘坐公交的比例较高，这可能表明老年人有较多的日常活动安排在早上，如购物、参加社交活动等。这样的数据有助于城市规划者更好地理解老年人的日常活动模式，从而优化公共交通服务，满足他们的出行需求。其次，通过比较不同时间段老年人的出行比例，可以分析交通政策对老年人出行的影响。例如，如果发现取消老年公交卡后，早高峰期间车内拥挤不堪，这可能说明免费公交政策对缓解老年人出行困难有所帮助，同时也反映了在制定交通政策时应考虑到老年人的特殊需求。此外，老年人出行特征的数据还可以用于评估城市公共服务设施的布局和功能。例如，老年群体出行目的以满足生活、文化和情感需求为主，这样的数据可以指导城市在规划老年大学、健身场所等公共服务设施时，更加注重其位置和功能的合理性，以满足老年人的实际需求。给交通政策的调整提供依据。根据早晚高峰不均衡数值，调整运营策略，如在早高峰期间车载电视投送针对老年人的广告。1.数据采集：通过线网分析平台，采集当日分时老年人的乘车数据 2.数据处理：统计当日早高峰和晚高峰的老年人乘车次数，并统计当日老年人的全部乘车次数。早高峰占比=当日老年人早高峰坐车次数/当日老年人全天坐车次数。晚高峰占比=当日老年人晚高峰坐车次数/当日老年人全天坐车次数。早晚高峰占比=早高峰占比+晚高峰占比。 早晚高峰不均衡数值=早高峰占比/晚高峰占比。
This dataset is designed to evaluate the travel demands of the elderly population in Keqiao District and the impacts of local traffic policies. First, analyzing the proportion of elderly bus riders during the morning peak hour can help assess their travel demands and preferences. For example, a high share of elderly passengers taking buses during the morning peak may indicate that they have abundant daily morning activities such as shopping, social gatherings, and daily errands. Such data can enable urban planners to gain a clearer understanding of the daily activity patterns of the elderly, thereby optimizing public transit services to better meet their travel needs. Second, comparing the travel proportions of the elderly across different time periods can be used to analyze the impacts of traffic policies on their travel behaviors. For instance, if severe overcrowding on buses is observed during the morning peak hour following the cancellation of the free senior citizen public transit card policy, this suggests that the free public transit benefit has effectively alleviated the travel difficulties faced by the elderly, while also highlighting the necessity of considering the special needs of the elderly when formulating traffic policies. Additionally, data on the travel characteristics of the elderly can also be utilized to evaluate the layout and functionality of urban public service facilities. Since the travel purposes of the elderly mainly focus on meeting their daily living, cultural, and emotional needs, such data can guide urban planners to prioritize the rationality of location and function when planning public service facilities such as senior universities and fitness venues, so as to better address the actual needs of the elderly and provide a scientific basis for adjusting traffic policies. For example, operation strategies can be adjusted based on the morning-evening peak unevenness index, such as displaying advertisements targeting the elderly on on-board TVs during the morning peak hour. 1. Data Collection: Collect time-segmental bus riding data of the elderly on the target day via the public transit network analysis platform. 2. Data Processing: Count the number of bus riding trips of the elderly during the morning peak hour and evening peak hour on the same day, as well as their total daily bus riding trips. Calculate the morning peak proportion as the ratio of the number of elderly bus riding trips during the morning peak to the total daily bus riding trips of the elderly. The evening peak proportion is calculated as the ratio of the number of elderly bus riding trips during the evening peak to the total daily bus riding trips of the elderly. The combined peak-hour proportion is the sum of the morning peak proportion and the evening peak proportion. The morning-evening peak unevenness index is obtained by dividing the morning peak proportion by the evening peak proportion.
