{"id":1875,"date":"2020-03-16T15:09:59","date_gmt":"2020-03-16T06:09:59","guid":{"rendered":"http:\/\/bebensiganteng.com\/?p=1875"},"modified":"2022-06-06T22:12:26","modified_gmt":"2022-06-06T13:12:26","slug":"hourtrip-the-technical-report","status":"publish","type":"post","link":"https:\/\/rahmat-hidayat.com\/?p=1875","title":{"rendered":"HourTrip – The Technical report"},"content":{"rendered":"\n

Update 2020\/06\/03<\/strong> Well, we’ve finally ended it, 1.5 years of collecting data, taking the photo of every single location, and making the MVP went down the drain.<\/p>\n\n\n\n

But worry not, although I’m getting older, this will not be my last venture, as long I can work 16-18 hours a day someday I will have a business.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Hourtrip is a travel website that I did with a few of my friends although we are still struggling on the business side, I’m quite proud of the algorithm.<\/p>\n\n\n\n

Have you gone on a trip and thought of \u201cI only have 6 hours to spend where should I go?\u201d well with Hourtrip you can! <\/p><\/blockquote>\n\n\n\n

The concept is pretty simple, have you gone on a trip and thought of “I only have 6 hours to spend where should I go?” well with Hourtrip you can! and it’s smart enough to know whether it’s time for lunch or dinner, which places you should go to, determine what time you should go back, etc.<\/p>\n\n\n\n

As an overview, the algorithm is pretty simple. Extract the user’s preference, use that data to filter the database, and then show the recommended locations.<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

To show the recommended locations, it needs to iteratively calculate the closest location from the original location, the graph below explains roughly how the iteration works.<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

Check the weather<\/strong>
I use OpenWeatherMap<\/a> API to get the info, at this point, I only check whether it’s bad or good, in the future, I’m planning to check whether the weather is good enough for travelling. <\/p>\n\n\n\n

For example, a drizzle might not stop you from going to that restaurant you like.<\/p>\n\n\n

\nURL = "http:\/\/api.openweathermap.org\/data\/2.5\/forecast\/?units=metric&amp;APPID=0cdde5a888c7dc5664225b6112dcd07d"\npath = URL + "&amp;lat=" + kwargs['origin']['lat'] + "&amp;lon=" + kwargs['origin']['lng']\nresult = json.loads(urlopen(path).read().decode('utf-8'))\n<\/pre>\n\n\n
Check the preferences<\/strong>
Checking the preferences is simply a hash lookup.<\/p>\n\n\n
\nif self.match_tags(self._types, v['tag']):\n  di = DestinationItem(data=v)\n  self._dest_list.append(di)\n<\/pre>\n\n\n
Check the walking duration<\/strong> and Location’s tiers<\/strong>
There are two components when checking the walking duration, the time to get from the current location to the next location, and the current location to the original point.<\/p>\n\n\n\n
Determining the walking speed is quite tricky and I have to admit is a mixture of real data and hunch, according to Wikipedia<\/a>, the average walking speed is 1.4 m\/s but for tourists, they might walk longer than that, due to the traffic, pausing for the sights, having unruly kids, etc. so I add another 1 second to anticipate that.<\/p>\n\n\n
\nWALKING_SPEED   = 2.4  #  +1 to anticipate sight seeing, traffic, etc.\n\ndef get_distance(self, node_origin, node_dest):\n\n    origin  = self.get_latlng(node_origin)\n    dest    = self.get_latlng(node_dest)\n    dist    = round(geodesic(origin, dest).kilometers * 1000)\n\n    return {\n        'dist_from_source': dist, # meters\n        'walking_time': round(dist\/WALKING_SPEED)\n    }\n<\/pre>\n\n\n
In the beginning, to calculate the nearest distance I use Google Map API, but boy was that expensive! Nearly bankrupt all of us so I use GeoPy library<\/a> instead.<\/p>\n\n\n\n
Once the walking duration is understood it’s easy to get to the nearest location, now this has to be tied with the location’s tier (score value). <\/p>\n\n\n
\ndef get_shortest_dest(self, node_origin, tier, p_dist=None):\n\n  dist    = None\n  index   = -1\n  arrival = None\n\n  for i, v in enumerate(self._dest_list):\n\n    if self.match_tags([v.tier], tier):\n\n      d = self.get_distance(node_origin,v)\n      # estimated arrival times\n      # TODO: there's a variable for arrival_time, change tothat\n      arrival = self._timestamp + datetime.timedelta(seconds=d['walking_time'])\n\n      if v.is_open(arrival):\n\n        if dist is None:\n          dist = d['dist_from_source']\n\n        if p_dist is None:\n\n          if d['dist_from_source'] <= dist:\n            dist    = d['dist_from_source']\n            index   = i\n        else:\n          # if between dest_a and dest_b subtier exist then calculate\n          if d['dist_from_source'] <= dist and d['dist_from_source'] <= p_dist:\n            dist    = d['dist_from_source']\n            index   = i\n\n  return { 'index': index, 'dist': dist, 'arrival': arrival}\n\ndef get_next_dest(self, node_origin):\n\n    result  = self.get_shortest_dest(node_origin, ['1','2'])\n    i       = result['index']\n    ii      = -1\n\n    # 2. if tier [1,2] undefined, search [3,4]\n    if i < 0:\n        result  = self.get_shortest_dest(node_origin, ['3','4'])\n        ii      = result['index']\n\n    else:\n        # if tier [1,2] exist, search for the closest [3,4] tier\n        # since the last tier has already been removed, then automatically the next tier will be refered\n        result  = self.get_shortest_dest(node_origin, ['3','4'], result['dist'])\n        ii      = result['index']\n\n    # return tier 3\/4\n    if ii >= 0 and len(self._result_dest) > 0:\n\n        tmp_tier = self._dest_list[ii].tier\n        tmp_all_tier = self._main_tmp.get_all_tier()\n\n        # if tmp_tier not in tmp_all_tier:\n        #     # ensure that each leg has only 1 3\/4 tier\n        #     self._main_tmp.add_tier(tmp_tier, ii)\n        #     return self._dest_list.pop(ii)\n\n        # TODO: this repetition, refactor\n        if self._dest_list[ii].is_gluttony_time(result['arrival']):\n\n            if tmp_tier not in tmp_all_tier:\n                self._main_tmp.add_tier(tmp_tier, ii)\n                return self._dest_list.pop(ii)\n\n        else:\n\n            if tmp_tier not in tmp_all_tier:\n                self._main_tmp.add_tier(tmp_tier, ii)\n                return self._dest_list.pop(ii)\n\n\n\n    if i >= 0:\n        # 1. save it to the main_temp for refference\n        self._main_tmp = self._dest_list[i]\n        return self._dest_list.pop(i)\n\n    return None\n<\/pre>\n\n\n
Each trip will be distributed between 1 premium locations’ tier and 3 ordinary location’s tier so if the user still has time to visit other places then the same pattern will be repeated.<\/p>\n\n\n\n
\"\"<\/a><\/figure>\n\n\n
\n\/\/ Calculating the time budget\n\nif(walking time + spending time + going home time < time budget) {\n   repeat;\n} else {\n   return result;\n}\n<\/pre>\n\n\n
And of course, the pattern may vary depending on distances and schedules.<\/p>\n\n\n\n
Check schedules<\/strong>
There are 3 schedules I need to check, locations’ schedule, public transport schedule, and whether it’s time for lunch\/dinner.<\/p>\n\n\n\n
Checking it is a simple lookup table comparison.<\/p>\n\n\n
\ndef is_gluttony_time(self, time):\n\n    # Restaurant 1100-1400, 1700-2100\n    # Cafe 0700-1100, 1300-1700\n    # Liquor 1700-2600\n    time_int = int(time.strftime('%H%M'))\n    time_slot = [\n        {\n            'open': [1100, 1700],\n            'close': [1400, 2100],\n            'tag': ['restaurant']\n        },\n        {\n            'open': [700, 1300],\n            'close': [1100, 1700],\n            'tag': ['restaurant','cafe']\n        },\n        {\n            'open': [1700],\n            'close': [2600],\n            'tag': ['liquor']\n        }\n    ]\n\n    for i, data in enumerate(time_slot):\n\n        a_open   = data['open']\n        a_close  = data['close']\n\n        for j, v in enumerate(a_open):\n            if time_int > v and time_int < a_close[j]:\n\n                # TODO: change self._types to tag\n                check = self.match_tags(time_slot[j]['tag'], self._types)\n\n                if check:\n                    self._raw['is_gluttony'] = True\n\n                return check\n\n    return False\n<\/pre>\n\n\n
So that’s the gist of it, I still have the plan to expand it, find some investors and do what all startup founders do drown in money and be famous or have a mental breakdown, hahaha.<\/p>\n\n\n\n
Icons made by dDara<\/a> from www.flaticon.com<\/a>
Icons made by catkuro<\/a> from www.flaticon.com<\/a>
Icons made by Smashicons<\/a> from www.flaticon.com<\/a><\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Update 2020\/06\/03 Well, we’ve finally ended it, 1.5 years of collecting data, taking the photo of every single location, and making the MVP went down the drain. But worry not, although I’m getting older, this will not be my last venture, as long I can work 16-18 hours a day someday I will have a […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[7,23,29,31,35,36,203,52],"tags":[],"_links":{"self":[{"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=\/wp\/v2\/posts\/1875"}],"collection":[{"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1875"}],"version-history":[{"count":40,"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=\/wp\/v2\/posts\/1875\/revisions"}],"predecessor-version":[{"id":2019,"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=\/wp\/v2\/posts\/1875\/revisions\/2019"}],"wp:attachment":[{"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1875"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1875"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rahmat-hidayat.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1875"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}