Car Park Overspill Study PROJECT DOCUMENTATION FINAL

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Preliminary Phase

Scope

Our sponsor requires us to finish 5 final reports of different development sites during the preliminary phase of our project. The necessary data and infographics are prepared by the sponsor and his tea, our job scope is to compile the report using the sample template and interpret the data to generate some insights which can be helpful for LTA future planning. A typical final report contains the following components:

1. Executive Summary
2. Site Background
3. Site Characteristics
4. Site Assessment
5. Survey Findings
6. Conclusion

“Executive Summary” gives a brief overview of the report and its conclusion. “Site Background”, “Site Characteristics” and “Site Assessment” provides the basic information of the site, such as the nature, the size, the transportation availability of the site and etc. Our focus is on “Survey Findings” which uses the data collected on both weekday and weekend at the site to generate some insights. The generated insights elaborate on the vehicles traffic pattern, the human traffic during different period of the day and most importantly whether there is overspill, this helps LTA understands the utilization rate of the car park and make certain adjustments in the future.

Actual Deliverables

By the end of the preliminary phase, our group has finished 5 final reports as planned at the beginning of the project. These 5 final reports are:

• Final Report - Greenwood Ave & Hillcrest Rd
• Final Report - Vivocity
• Final Report - Frankel Ave
• Final Report - Jalan Mata Ayer
• Final Report - Yuhua Market & Hawker Centre

Phase 2

INTERIM

FINAL

Revised Methodology

We have revised our goal of Phase 2, instead of building a simulation tool with projection of a car park together with its surrounding environment such as streets and roads, now we aim to let the tool to simulate a car park inside traffic only. Considering the raw data at hand, it includes surveyed car parks inbound and outbound traffic. The data become more meaningful when we uncover the pattern of car arriving at a car park and apply the pattern into a model which simulates the traffic inside a car park only. Therefore, we redefined the Phase 2 objective, and accordingly we update the preparing data process to suit the updated needs.

Data Preparation

To help us understand the traffic situation inside a car park, we process the raw data in the following steps:

1. Based on the nature of the car park's associated site, hence, we categorized 4 sets of data - Shopping Malls, F&B Clusters, Community Clubs, and Hawker Centres. The following table shows the basic information of each category:

Site Category	Shopping Mall	Community Club	Hawker Centre	F&B Cluster
Num of Samples	17	10	10	7
Survey Periods	10am - 9pm (Weekday & Weekend)	9am - 6pm (Weekend), 2pm - 10pm (Weekday)	7am - 3pm (Weekday & Weekend), 6pm - 8pm(Weekday)	10am - 10pm (Weekday & Weekend)

2. Organize the Inbound Traffic data of each site in a site category, such that we have a collection of data showing the number of arriving cars at each car park in each 15 minutes interval from 7:00 am to 10:00 pm. The following screen shot shows an example of the collection of data of Shopping Mall Category sites:

3. We understand that the car parks' associated development site have varied capacities in accommodating patrons or customers, so we normalized the data , by applying below formula:

The following screen shot shows an instance of data has been normalized:

4. After normalizing the data, in each site category, we still found a few outliers with normalized value deviating from the mean value greatly. To ensure that the data as input is effective in representing the general cases to the agent-based model we build, we have to remove those outliers. The following plot chart shows an example of the effect before and after removing the outliers:

Implementation of Agent-based Model

Agent-based modelling is particularly interesting to us in this simulating and assessing car park effectiveness problem, as this problem involves interactions between different objects (e.g. cars and parking lots, cars and junctions etc.) and also interactions among same objects (e.g. cars) in a car park. In the meantime, the rule or behaviour followed by each object is relatively simpler to find out and define. In the context, users are interested to know not only the qualitative results of assessing the effectiveness of a car park, but also the detailed interactions and even the cause of a particular interaction. Agent based modelling is approved to be a good approach in applying into our problem context.

Model Introduction

The following diagram abstractly shows the inputs the simulation model needs and the output it generates. It takes two sorts of inputs: 1. built-in configurations: Processed data in earlier stage is used here are configuration for the model when simulation the pattern of arriving cars at a car park. It will be scaled up proportionally to one of the user inputs - Car Park Capacity - and generate the number of arriving cars in each time interval.

2. User inputs and heuristic algorithm configuration parameters: There are 4 key user inputs which have to be specified before the simulation of the model. They are “Development Site Type”, “Peak/Non-Peak”, “Maximum Head Count” and “Number of Parking Space” as highlighted in orange in the pictures above. Based on “Development Site Type” and “Peak/Non-Peak”, we will retrieve the processed data which belongs to the 2 categories and this data will be used to calculate the “incoming Traffic / Head Count” at different time periods of the day. Next, the user will use “Maximum Head Count” to specify the rough size of the development site, for example, 1000 indicates that the development site will hold roughly maximum 1000 persons. We multiple the head count with the incoming traffic to find out the “Size of the Incoming Traffic” which is relative to the size of the development site. Lastly, we will generate the cars at different periods based on this data, the cars will interact with each other and also the world to see whether the “Number of Parking Space” entered by the user is enough for such development site.

3. Output Charts: The Model will monitor and display the Occupancy Rate, Number of Overspills, and Average Waiting Time dynamically.

Defining agent behavior

In Agent-based Modelling, agents can be categorized into animated ones and inanimated ones. In NetLogo, the animated agents are called "turtles", and the inanimated agents are called "patches". In our case, the only "turtle" is car object. The rest objects like roads, traffic cross corners, parking lots, entrance, exit and lift are "patches".

Define Parking Car's behavior:

There have been some research papers discussing the behavior of the drivers when they park cars in a car park. We selected a few relevant heuristic parking algorithms and compare them in the following table:

Model Interface